{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import random\n",
    "import pickle\n",
    "import itertools\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.metrics import label_ranking_average_precision_score, label_ranking_loss, coverage_error \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from scipy.signal import resample\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "import pickle\n",
    "\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Conv1D, MaxPooling1D, Softmax, Add, Flatten, Activation# , Dropout\n",
    "from keras import backend as K\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"D:/cao/kaggle/ECG/mitbih_train.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87554 entries, 0 to 87553\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 125.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"D:/cao/kaggle/ECG/mitbih_test.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
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    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "[5 rows x 188 columns]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集和测试集合并在一张表里\n",
    "\n",
    "df = pd.concat([df, df2], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 109446 entries, 0 to 21891\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 157.8 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    90589\n",
       "4.0     8039\n",
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看 5 个类的样本数\n",
    "\n",
    "df[187].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = df.values\n",
    "X = M[:, :-1]\n",
    "y = M[:, -1].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109446, 187)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109446,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除不再使用的变量\n",
    "\n",
    "del df\n",
    "del df2\n",
    "del M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制 5 种不同类别的心电图\n",
    "\n",
    "C0 = np.argwhere(y == 0).flatten()\n",
    "C1 = np.argwhere(y == 1).flatten()\n",
    "C2 = np.argwhere(y == 2).flatten()\n",
    "C3 = np.argwhere(y == 3).flatten()\n",
    "C4 = np.argwhere(y == 4).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 取每个类别的其中一组数据来展示\n",
    "\n",
    "# 限定 x 的范围，即限定时间的范围\n",
    "x = np.arange(0, 187)*8/1000\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "#plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "#plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "#plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "#plt.plot(x, X[C4, :][0], label='Cat. Q')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C0\", fontsize=20)\n",
    "plt.ylabel(\"Amplitude\", fontsize=15)\n",
    "plt.xlabel(\"Time (ms)\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*8/1000\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "#plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "#plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "#plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "#plt.plot(x, X[C4, :][0], label='Cat. Q')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C1\", fontsize=20)\n",
    "plt.ylabel(\"Amplitude\", fontsize=15)\n",
    "plt.xlabel(\"Time (ms)\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*8/1000\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "#plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "#plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "#plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "#plt.plot(x, X[C4, :][0], label='Cat. Q')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C2\", fontsize=20)\n",
    "plt.ylabel(\"Amplitude\", fontsize=15)\n",
    "plt.xlabel(\"Time (ms)\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*8/1000\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "#plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "#plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "#plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "#plt.plot(x, X[C4, :][0], label='Cat. Q')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C3\", fontsize=20)\n",
    "plt.ylabel(\"Amplitude\", fontsize=15)\n",
    "plt.xlabel(\"Time (ms)\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GuAQAAICkTDwtTkp0LkmcGAcAQL4jXAIAAEBSJp4WJ0n1ZaPhEnuXAADIa4RLAAAASMrI2ELvkzqXWOoNAEB+I1wCAABAUsKn6VxqZywOAIC8RrgEAACApJw8FldcWKBgoEBHCJcAAMhrhEsAAABISiQWl89r5PGY8cvqQwF2LgEAkOcIlwAAAJCUSDQ+3rU0pr4soI7+sEMVAQAANyBcAgAAQFJGYvHxZd5j6kMBdfSdcKgiAADgBoRLAAAASMrpOpe6BsKKjp4kBwAA8g/hEgAAAJISicZVeFLnUl0ooLiVugcjDlUFAACcRrgEAACApIRjp4ZLDWUBSWKpNwAAeYxwCQAAAEmZbCyuLjQaLvURLgEAkK8IlwAAAJCUycbi6sc6l1jqDQBA3iJcAgAAQFJGYqd2LlUWF8rnNeroDztUFQAAcBrhEgAAAJIyWeeSx2NUGwzoCDuXAADIW4RLAAAASEpkkoXeUmKpNzuXAADIX4RLAAAASMpkC70lqa4swGlxAADkMcIlAAAAJCUSjcs3SedSfSjRuWStdaAqAADgNMIlAAAAJCUSi8s/SedSfSigEyMx9Q9HHagKAAA4jXAJAAAASZlsobeUGIuTxFJvAADyFOESAAAAknKmhd6SWOoNAECeIlwCAABAUk630Ls+RLgEAEA+I1wCAABAUk630Lsm6JckdQ2GM10SAABwAcIlAAAAnFU8bhWN20k7lwI+r4KBAnUNEC4BAJCPCJcAAABwVpFYXJIm3bkkJbqXCJcAAMhPhEsAAAA4q7FwyX+6cKmUcAkAgHxFuAQAAICzikTP3LlUGwqoc4CF3gAA5CPCJQAAAJzVeLg0yc4lic4lAADyGeESAAAAzmpkdCzOd7pwKejX8UhMx8PRTJYFAABcgHAJAAAAZ3XWsbigX5LoXgIAIA8RLgEAAOCswmcJl2rGwqVBwiUAAPIN4RIAAADOauy0uLOGS3QuAQCQdwiXAAAAcFZjY3H+0+xcGhuL6+znxDgAAPIN4RIAAADOaixc8p2mc6miuFBej2EsDgCAPES4BCDvRWNxPbC7S7G4dboUAHCtsdPiCk/TueTxGFWXFjIWBwBAHiJcApD3vvfwC7rh+0/qtt/tkLUETAAwmbOdFidJtcGAOgmXAADIO4RLAPLaSCyuHz66X6X+Av348VZ9/f4Wp0sCAFc620JvKbHUm84lAADyD+ESgLx217Z2tfcN68tvOU/Xnj9bX7h7t36x+aDTZQGA64SjZx6Lk6SaUsIlAADyUYHTBQCAU6y1+u5D+7SgpkSXLavVxUtq1D0Y1i2/3qbq0kJdtqzuRbff331c33pgr55p69OP3nWBakMBhyoHgMxLaiwu5Ff3YFixuJXXYzJVGgAAcBidSwDy1uP7erT9UL9uvGiBPB6jwgKPvvn21VrRENL779iqpw4ckyQ939GvD/3sKV32/zbq108dUkvngD71h50OVw8AmXW2hd5SYiwubqWe45FMlQUAAFyAcAlA3vrew/tUWVKo16+aPX5Zqb9A33/nBaoNBvTuH27SjT/apCu//JD+8twRvffiBXr4ny/VBy5drD882677d3U6WD0AZFYynUs1pX5JYjQOAIA8Q7gEIC/t7RrUvc916u1r5yng877oupqgXz969xp5jNHm1mP6+1cs0SMfvUy3vHq5aoMB3bxhgRbWlOhjd27XUCTq0E8AAJmV7FicJHUODGekJgAA4A7sXAKQl7738AsqLPDoHS+fN+n186tLdN8/bFBhgUdFhS8On/wFXn322nP1lu88rv+4d49uuWp5JkoGAEdFYnEZIxWcYZdSTWliFx2dSwAA5Bc6lwDknZ7jEf33lja9/vzZqh4d4ZhMWbHvlGBpzMsWVOktzXP1nw+/oJ2H+9NVKgC4RiQal8/rkTFnCJeCo2Nxg4RLAADkE8IlAHnnJ4+3KhyN6z0XzZ/R49xy1TJVFPt0y53bFIvbFFUHAO4UicXlP8Myb0kqKvQq6C9QZz/hEgAA+YRwCUBeGR6J6fbH9mvD0hotrgvO6LHKiwv18atX6JmDvbrjidbUFAgALhWJxs+4b2lMTdBP5xIAAHmGcAlA3ohE4/rpEwfUPRjRe9cvSMljvu6ls7R+cbX+7592qaOPBbYAcley4VJ10M/OJQAA8gwLvQHkJGut/rzjiJ5p61VL56D2dg6qtWdIsbjVObNDunBhVUqexxijT//NOXrVlx/Ue2/frNvfvUYVJYUpeWwAcJNILLlwqTbo1w520QEAkFcIlwDkpD/vOKKbf7JFPq9RU1WJltYHddW5DVpUW6oNS2vOuJB2quZVlegbb1ulm3+yVdd953H95MaXjS+1BYBcMbbQ+2xq6FwCACDvEC4ByEk/e/KAGsoCeuCfLk3qm/aZumxZnX7wzgt044826y3ffkx3vPdlaigrSvvzAkCmjMTiKkwyXBoMRzUUiaq4kLeaAADkA3YuAcg5h3pP6ME9XXpT89yMBEtj1i2q1o/fs0ZdA2G96VuP6cDRoYw9NwCkWzjZhd6lic5NupcAAMgfhEsAcs4vNx+UJL1p9ZyMP3dzU6V++t61GgxH9aZvP6qWzsGM1wAA6ZDsQu/aUEAS4RIAAPmEcAlATonFrX6x6aAuWlStuZXFjtRw7pwy/fymtYrFpbf95+MaicUdqQMAUikSi8tP5xIAAJgE4RKAnPLQni4d7hvW9WsaHa1jWX1I//LaFTrSH9Zz7ZyaBCD7TWWhtyR1Ei4BAJA3CJcA5JT/2nRQlSWFesXyOqdL0ZqmSknSpv3HHK4EAGYu2YXelSWF8noMnUsAAOQRwiUAOaNrIKx7dh7RG1bNzugi79OpLwtodnmRtrT2OF0KAMxYsjuXvB6jqpJCwiUAAPKI85++ACBFfr21TdG41VsucHYkbqILmiq0af8xWWudLgUAZiTZcElKjMZ1DgynuSIAAOAWhEsAcoK1Vv+16aAuaKrQotpSp8sZt7qpUl0DYR3sOeF0KQAwI5FY8uFSbdCvrkE6lwAAyBeESwBywpMv9Ghf93FXdS1Jic4lSdq0n9E4ANktHE1u55KU6FxiLA4AgPxBuAQgJ/zXpoMK+gv0mnMbnC7lRZbUBhUMFGhzK0u9AWS3qY7FdQ9GFIszEgwAQD4gXAKQ9fqGRvQ/29p1zfmzVFTodbqcF/F4jFbPq2CpN4Csl+xpcZJUGwwoFrc6NhRJc1UAAMANCJcAZL3fPnNI4Whc17lsJG5M87wK7T4yqF4+ZAHIUtFYXHGrKXUuSWI0DgCAPEG4BCCrDY/E9O0H9umlc8p0zuwyp8uZVHNTpSRp6wFG4wBkp0gsLmnq4VIn4RIAAHmBcAlAVvvewy/oUO8J/fOrlzldymm9dE65CjxGm/YTLgHITpHoaLiU9FgcnUsAAOQTwiUAWauzf1jfuL9FV6yo04ULq50u57SKCr06Z3aZthAuAchSY+GSL8nOpepSwiUAAPIJ4RKArPWFu3cpEovr1quWO13KWTXPq9DTbb0KR2NOlwIAUzY2FudPsnOpxF+gkkKvOgeG01kWAABwCcIlAFlp+6E+/XJLm961br6aqkucLuesmpsqFYnGtf1Qv9OlAMCUjY/FJdm5JEm1oQCdSwAA5AnCJQBZx1qrT/1hpyqLC/WByxY5XU5SVs+rkCRt3t/jcCUAMHVTXegtSTWlfsIlAADyBOESgKzzp+0devKFHn3kiiUKBXxOl5OUmqBf86tLtLmVvUsAss9UF3pLib/3CJcAAMgPhEsAssrwSEyf/eNzWlYf1Fua5zpdzpSsnlehLa3HZK11uhQAmJKpLvSWCJcAAMgnhEsAssoPHtmvgz0n9PGrV6hgCt+gu0HzvAr1HI9oX/dxp0sBgCkZH4ubYufSQDiqExEOMgAAINdl1yczAHmtb2hEX7+/Ra9YXqd1i6qdLmfKmpsqJUlb9jMaByC7TGehd03QL0l0LwEAkAcIlwBkjXufO6LBcFR/e+lCp0uZloU1Jaoo9mkTS70BZJmxcMk/ldPixsKlweG01AQAANyDcAlA1rh7Z4fqQwG9dE6506VMizFGq+dVagtLvQFkmWmdFkfnEgAAeYNwCUBWOBGJ6YHdXbpiZZ08HuN0OdPW3FShfd3H1T3Ihy0A2WN8ofcUdy5JUifhEgAAOY9wCUBWeLilW8MjcV2xot7pUmbkgqYKSaJ7CUBWGZlG51JViV8eQ+cSAAD5gHAJQFb4844OBQMFetmCSqdLmZFzZpcp4PPosb1HnS4FAJI2vtB7Cp1LXo9RVamfcAkAgDxAuATA9aKxuP7y3BFdvqx2SiMZbuQv8GrN/Co93NLtdCkAkLTwNE6Lk6SaUj9jcQAA5IHs/pQGIC9sbj2mY0MjetXK7B6JG3Px4mq1dA7qcO8Jp0sBgKSMLfSeymlxUmLvUucAp8UBAJDrCJcAuN6fd3SosMCji5fUOF1KSqxfnPg5Ht5D9xKA7DCdhd6SNKu8SId7CZcAAMh1hEsAXM1aq7t3HNH6RdUq8Rc4XU5KLKkrVW3Qrwf3dDldCgAkJRKNy+sx8k7xtM7GymL1HI9oMBxNU2UAAMANCJcAuNrO9n4d6j2RMyNxkmSM0frFNXqkpVvxuHW6HAA4q5FYfErLvMfMrSySJB3sGUp1SQAAwEUIlwC42t07jshjpMuX1zpdSkqtX1ytY0Mj2nG43+lSAOCsItH4lJd5S4nOJUk6QLgEAEBOI1wC4Gp/3tGh5nmVqir1O11KSq1bVC1JjMYByAqR2PTCpbkViXCJziUAAHIb4RIA1zpwdEjPdwzoipV1TpeScjVBv1Y0hPQQ4RKALBCOTm8srrzYp1J/AeESAAA5LuPhkjHmSmPMLmNMizHmo5Nc32iMud8Y85Qx5lljzFWZrhGAO9y9s0OSdMWK3Nm3NNH6JdXa0npMx1l0C8DlpjsWZ4zR3MpiHTx2Ig1VAQAAt8houGSM8Ur6uqRXS1oh6XpjzIqTbvYxSb+w1p4v6TpJ38hkjQDc4+4dR7SsPqjGqmKnS0mLixfXaCRm9cQLR50uBQDOaLoLvSVpbkURnUsAAOS4THcurZHUYq3dZ62NSPq5pGtOuo2VFBr97zJJhzNYHwCX6B4Ma3Nrj67IoVPiTrZ6XoX8BR49uLvb6VIA4Iym27kkJZZ6Hzw2JGs5HRMAgFyV6XBptqSDE/7cNnrZRLdJersxpk3SXZI+mJnSALjJfc91Km6lV+XgvqUxAZ9XL1tQxd4lAK433YXekjS3sljDI3F1DYZTXBUAAHCLTIdLZpLLTv4a63pJP7TWzpF0laQfG2NOqdMYc5MxZrMxZnNXFx/MgFwyPBLTtx7YqwU1JVrREDr7HbLYxYurtbfruA73so8EgHtFprnQW0p0LkmcGAcAQC7LdLjUJmnuhD/P0aljb++R9AtJstY+JikgqfrkB7LWfsda22ytba6pqUlTuQCc8M2Ne7Wv+7hue+1KGTNZJp071i9O/P318B5G4wC4VyQal2/anUtFkqSDPYToAADkqkyHS5skLTbGzDfGFCqxsPt3J93mgKTLJckYs1yJcInWJCBPtHQO6psb9+qa82bp4iW5HxwvqStVbdCvBxmNA+BikZiddufSnIpE59IBOpcAAMhZGQ2XrLVRSR+Q9GdJzylxKtwOY8ynjDGvG73ZP0h6rzHmGUk/k/ROywZIIC/E41a33rlNRYVeffzqkw+SzE3GGK1fXKOHW7oVi/NXHQB3ikRj8k+zcyng86o26GcsDgCAHFaQ6Se01t6lxKLuiZf9y4T/3ilpXabrAuC8X21p05Mv9Ojf3nCuqkv9TpeTMesXV+u/t7Zpx+E+vWROudPlAMApZrLQW0rsXaJzCQCA3JXpsTgAmFT3YFifues5rWmq1JtWzz37HXLIukWJtXIPsXcJgEvNZKG3lDgxru0YO5cAAMhVhEsAXOHTf9ipoUhUn339OfJ4cnuJ98lqgn6taAjpIfYuAXCpxELv6f/dPLeyWO19JxSJxlNYFQAAcAvCJQCOe2hPl37z9GG9b8MiLaoNOl2OI9YtqtLW1l6FozGnSwGAU4zErAq93mnff25FkeJWOtxL9xIAALmIcAmAo6KxuD72m+1aUF2i92/p9aByAAAgAElEQVRY6HQ5jlk9r1KRWFzbD/U7XQoAnCISnfnOJUk6eIy9SwAA5CLCJQCOevpgr1qPDunDr1yigG/634pnu1XzEou8nzpwzOFKAODFrLUzXug9dzRcYqk3AAC5iXAJgKM27uqS12N0yZIap0txVG0woDkVRdrSSrgEwF0iscSeJP8MwqW6UECFXo8O9jAWBwBALiJcAuCojbs7taqxXGVFPqdLcdzqeRXaeuCYrLVOlwIA48aWcPu801/o7fUYza4o0kE6lwAAyEmESwAc0zkwrO2H+rVhaa3TpbjCqsYKHekP63DfsNOlAMC4kVgi8C70zuxt45yKInYuAQCQowiXADjmwd3dkpT3I3FjVjVWSBKjcQBcZaxzqbBgZnvxGiuL2bkEAECOIlwC4JiNuzpVE/Rr5ayQ06W4wrKGoIp8Xm0lXALgIn8Nl2b2tnFuZbF6h0Y0MDySirIAAICLEC4BcEQ0FtdDe7p1yZIaGTP9PR65xOf16CVzyjgxDoCrRGIxSTMPlxpHT4xjqTcAALmHcAmAI55p61XfiRFtWMpI3ESr51Vox+F+DY/EnC4FACRJ4bHOpRks9JakuRWJcInROAAAcg/hEgBHbNzVJY+R1i8iXJpoVWOFonGrZ9v6nC4FACSlbixurHOpjaXeAADkHMIlAI54YHeXVjVWqKzY53QprnJ+Y7kklnoDcI+/nhY3s4XeZcU+BQMFdC4BAJCDCJcAZFz3YFjPtvUxEjeJqlK/5leXaCt7lwC4RKo6l6RE99JBwiUAAHIO4RKAjHtwd5ckacPSWocrcafzG8v11IFjstY6XQoApGyht5TYu0TnEgAAuYdwCUDGbdzVpepSv1Y0hJwuxZVWNVaoezDCBzAArjDWueSb4UJvSWqsKlbbsROKxwnPAQDIJYRLADIqFrd6cE+XLllSI49n5h9UctHqeRWSxGgcAFcYOy3On5LOpSKFo3F1DYZn/FgAAMA9CJcAZNQzbb3qHRph39IZLKkLqtRfoK2tvU6XAgApW+gtSXNHT4xj7xIAALmFcAlARm3c1SWPkdYvrna6FNfyeoxeOreME+MAuEIqF3qPhUuM/QIAkFsIlwBk1AO7OnV+Y4XKiwudLsXVVjdW6PmOfh0PR50uBUCei0RTt9B7dnmRjJEO9pyY8WMBAAD3IFwCkDFHB8N69lCfNixhJO5szp9XobhNjBECgJMisdQt9A74vKoLBnTwGJ1LAADkEsIlABmzcVeXrJU2LK11uhTXWzV3dKk3o3EAHJbKsThJaqwsZiwOAIAcQ7gEICOGR2L6yn17tKC6RCtnhZwux/XKin1aVFuqrQfoXALgrMj4Qu/UvG2cU1mkNsIlAAByCuESgIz46n171Hp0SJ++9hx5PDMfrcgHqxrLtfXAMVlrnS4FQB6LROMq9HpkTGr+7p5bUaz2/mGFR3c5AQCA7Ee4BCDtdnUM6NsP7NMbV8/RhQs5JS5Zq+dVqHdoRPu6jztdCoA8FonGUzYSJyXG4qyVDvcOp+wxAQCAswiXAKRVPG51653bFAwU6NarljtdTlZZPS+xd2nz/h6HKwGQzyKxWErDpbmVxZKkg4zGAQCQMwiXAKTVzzYd0JbWY/rYa1aosqTQ6XKyysKaUtWHAnpgd5fTpQDIY5FoPCUnxY2pDwUkSUf66VwCACBXEC4BSJvOgWF9/o/P68KFVXr9qtlOl5N1jDG6ZEmNHtrTrejoUeAAkGkjMZvSzqXakF+S1DkQTtljAgAAZxEuAUibT/1+p8LRuD5z7bkpWwSbbzYsrdHAcJRT4wA4Zmyhd6oEfF6FAgXqpHMJAICcQbgEIC3u39WpPzzbrg9eukjzq0ucLidrrVtcrQKP0cZdnU6XAiBPhaNxFRZ4U/qYtaEAnUsAAOQQwiUAKRePW932ux1aVFuq/33JQqfLyWqhgE+r5lVo4y72LgFwRiSW2tPiJKku5GfnEgAAOYRwCUDKbT1wTK1Hh/TByxal/ANJPtqwtEY72/sZIQHgiEg0psIULvSWpNognUsAAOQSPvUBSLm7tnWosMCjy5bVOl1KTtiwJPE6buTUOAAOiERT37lUG/Krsz8sa21KHxcAADiDcAlASsXjVn/c3q6LF9coGPA5XU5OWN4QVF3IrwcYjQPggJGYTelCbynRuRSJxdV3YiSljwsAAJxBuAQgpZ5u61V737Be85J6p0vJGcYYXbKkRg/t6VI0Fne6HAB5Jh2dS3UhvyTpSD+jcQAA5ALCJQApddez7Sr0enT58jqnS8kpG5bWqn84qqcO9jpdCoA8k1joneLT4oIBSVLnALvkAADIBYRLAFLGWqs/bu/Q+sXVCjESl1LrFlXL6zHauKvT6VIA5JlINC5fihd607kEAEBuIVwCkDLPtPXpUO8JvfrcBqdLyTllRT6tbqzQRvYuAciwcDQuf6oXetO5BABATiFcApAyf9zWLp/X6JWMxKXFJUtrtONwPx/GAGTUSCye8oXeRYVeBQMF6qRzCQCAnEC4BCAlrLX6n23tWreoWmXFjMSlw4alNZLEqXEAMiodC70lqTboJywHACBHEC4BSIlth/rUduyErmIkLm1WNIRUG/Rr427CJQCZk1jonfq3jHWhADuXAADIEYRLAFLirm0dKvAYXbGCkbh0McbokiU1emh3l6KxuNPlAMgDsbhVLG7lS/FYnETnEgAAuYRwCcCMWWt117Z2XbioWuXFhU6Xk9M2LK1V/3BUTx/sdboUAHkgEk0E2ensXLLWpvyxAQBAZhEuAZixHYf7daBnSK85t97pUnLeRYur5fUYTo0DkBGR0S7JVC/0lqSaoF+RaFz9J6Ipf2wAAJBZhEsuYa3VniMDOtR7wulSgCm7a1u7vB6jV64gXEq3siKfVjWW68E9hEsA0m+sc8mfps4lSTrCaBwAAFmPcMlFXvu1h/XDR15wugxgSsZH4hZWqbKEkbhMWLugStsP9WkwzLf9ANJrvHMpTafFSVInS70BAMh6BU4XgARjjGaVFam9j2/v4F6PtHRrx+G+F102MBzV/qNDuunihQ5VlX+amyoVt9LTB3p10eJqp8sBkMPGOpfSsdB7vHOpn/c+AABkO8IlF6kvCxAuwbWO9A/rPT/apOGRU08pCwYK9KqVnBKXKec3lssYaXNrD+ESgLRK50Lv2tBo59IAnUsAAGQ7wiUXqS8L6Il9PU6XAUzqa/e1KBqzuufvL9as8qIXXefzetLywQOTCwV8WlYf0ub9x5wuBUCOG0njQu/iwgIF/QV0LgEAkAMIl1ykoSygI/3DisWtvB7jdDnAuANHh/SzJw/oujVztbgu6HQ5kNQ8r0K/3tqmaCyugjR86AMASQqnsXNJkmpCfnXRuQQAQNbjE4mL1JcVKRq3OjrImyy4y5f/sltej9EHL1vsdCkY1dxUoeORmJ7vGHC6FAA5LJ1jcZJUFwzQuQQAQA4gXHKRhtHFluxdgpvsPjKgO586pHde2DS+fBXOa26qlCRt3s8oLYD0iaRxLE5K7F1i5xIAANmPcMlFGsrHwqUTDlcC/NUX796tksIC3XwJp8G5yezyIs0qC2hzK3uXAKRP2juXQonOJWttWh4fAABkBuGSizSUJZYk07kEt3i2rVd/2tGh965foIqSQqfLwUlWN1Vq8/5jfCgDkDbjC73TFC7VBv0KR+PqH46m5fEBAEBmEC65SEWxT4UFHnUQLsElvnD3blUU+/Tui5qcLgWTuKCpQh39wzrUS7cjgPQY71xK21hcomu7k71LAABkNcIlFzHGqKEsQOcSXOHxfUf14O4uvX/DIgUDPqfLwSRWz6uQJG3ez2gcgPRI91hcbdAvSexdAgAgyxEuuUx9KEDnEjLKWquRWPxFvyLRuL7w512qC/n1v14+z+kScRrL6kMq9RdocytLvQGkRzjNY3FjB0VwYhwAANmtwOkC8GKzyov4oIiMsdbqfT/Zqj/t6Jj0+s9ce44CPm+Gq0KyvB6j8xvL6VwCkDZpH4ujcwkAgJxAuOQy9WWJzqV43MrjMU6Xgxz35x0d+tOODr1x9Rw1VRW/6LpSf4He0jzXocqQrAuaKvWle3er78SIyooYXwSQWukeiyvxF6jUX0DnEgAAWY5wyWUaygIaiVkdPR5Rzei3eUA6hKMxfeau57S0LqjPv/5cFaTpW2mkV/O8ClkrbT1wTJcurXW6HAA5Zvy0uDT+G1Eb9NO5BABAluPTpMvUj+4eYO8S0u0Hj+zXwZ4T+tjVywmWsth5jeXyeoy2MBoHIA0i0bg8Rmn9d6I25Oe0OAAAshyfKF2moaxIktTex9HiSJ+ugbC+dl+LXrG8VusX1zhdDmaguLBAK2eFtGk/u9oApF4kFk/bSNyY2mCAziUAALIc4ZLL1JeNdi7xDR7S6Iv37NLwSEy3XrXc6VKQAs3zKvVMW+/4bhQASJVINC5fmrtb60J+HekflrU2rc8DAADSh3DJZapKClXo9ehwL+ES0mPH4T79fNNB3XBhkxbUlDpdDlKgualCwyNx7Tjc53QpAHJMOBqXPwOdS8MjcQ2Eo2l9HgAAkD6ESy7j8RjVlfnVwVgc0sBaq3/9w06VF/n0ocsWO10OUqR5XoUkaUsre5cApNZILJ7WZd5SYueSJPYuAQCQxQiXXKghVKR2FnojDe7eeUSP7+vRR165RGXFHFufK2pDAc2rKmbvEoCUi0Qzs3NJkjr72bsEAEC2IlxyofqyADuXkHInIjF99q7ntLi2VNevaXS6HKTY6nkV2tJ6jJ0lAFIqI+HSaOfSkQHe+wAAkK0Il1yooSyg9j4WWyJ1DvYM6Q3ffFQHeob0ideuTOuR0nDGBU2V6h6MaP/RIadLAZBDIrFMLPSmcwkAgGzHJ0wXqi8LKBKN69jQiNOlIAc8vKdbr/3awzp4bEjfv+ECXbS42umSkAbrFib+7/qbpw45XAmAXJKJzqVSf4GKC706QrgEAEDWIlxyoYayIknS4V6WemP6rLX6zoN79Y7vP6HaoF+//8BFunRZrdNlIU0aq4p1+bJa/eTxVg2PxJwuB0COiGRgobeU6F7qZCwOAICsRbjkQg1lifbwDpZ6Y5qGIlF98GdP6bN3Pa8rz6nXne9fp6bqEqfLQprduH6Bjh6P6E66lwCkSCY6lySpJuhnLA4AgCxGuORCY+FSO0u9MQ3WWr3/jq26a1u7/vnKZfr6W1epxF/gdFnIgLULKnXO7JD+86F9isfZ2QZg5iLRuPwZCJfoXAIAILsRLrlQValfBR6jjj7G4jB1v3vmsDbu6tLHr16h921YKGOM0yUhQ4wxuvGiBdrbdVwbd3c6XQ6AHJCJhd6SVBv060h/mMNMAADIUoRLLuT1GNWFEifGAVPROxTRv/5hp146t1zveHmT0+XAAa95SYMaygL6z4decLoUADkgU2NxdSG/TozENBiOpv25AABA6hEuuVR9WUDtvYRLmJrP//F5HRsa0eeuPVdeDx1L+cjn9eidFzbp0b1HteNwn9PlAMhyIxla6F0bTKwE4MQ4AACyE+GSSzWUBdTBziVMwZMv9Ojnmw7qxovma8WskNPlwEHXrWlUSaGX7iUAM5apzqXakF+S2LsEAECWIlxyqYaygNr7TrB7AOO+dt8eXfedx7T90KndKOFoTLfeuU2zy4v0d69Y7EB1cJOyIp/efMFc/f6Zw2pndxuAGchYuDTaucSJcQAAZCfCJZeqLyvS8EhcfSdGnC4FLvCjR/frC3fv1tYDvbrm64/os3c9pxOR2Pj1335gn1o6B/Xpa89RcSEnw0F697r5ilurHz3a6nQpALJYOENjcXV0LgEAkNUIl1yqoSzxDR5LvXHXtnbd9vsdesXyOj1+y+V6c/McfefBfbriyw/ooT1d2tc1qK/d36KrX9KgS5fWOl0uXGJuZbFefU6DfvpEq46zIBfANFhrM9a5VOovUJHPy84lAACyFOGSS9WPhksdhEt57Yl9R/Xh/3pa588t11evP1+VJYX63Otfop/ftFY+j0f/63tP6s3fflz+Ao/+5bUrnC4XLnPj+vnqH47qF5sPOl0KgCw0EkuM5meic8kYo7qQX50DhEsAAGQjwiWXGutcOsy+lLy1q2NAN96+WXMrivS9Gy5QUaF3/Lq1C6p019+t1wcuXaT+EyP6+GtWjO+rAMac31ihl84t16+3HnK6FABZaGz8euK/P+lUGwqog/c9AABkJcIll6oNBuT1GDqX8tTh3hO64ftPqrjQqx+9e40qSgpPuU3A59U/vmqpdnzqVXrzBXMdqBLZYMOSGu043Mf+NgBT1jWYeA9SE/Rn5PlmlQV0uJf3PQAAZCM2/7qU12NUG/SzcykPxOJWB3uG1NI5qJauQbV0DurRlm4dD0f1i5tfrjkVxWe8vy8D4wrIXmsXVOk//rJHm/f36PLldU6XAyCLjJ3clrFwqbxIR/rbFYtbeT0mI88JAABSg3DJxerLAnQu5bhfb23TLb/epnA0Pn5ZbdCvxXWl+vArlmh5Q8jB6pALzm8sV2GBR4/vO0q4BGBKxvYfZWrselZ5kaJxq66B8PjuSQAAkB0Il1ysoSygXR0DTpeBNOkdiuiTv9+pZfVBvW3tPC2qLdXCmlKVFfmcLg05JODzalVjuR7bd9TpUgBkmc6BxBdctaHMdC7NLi+SJB3qPUG4BABAlmGexsXqQ0Vq7xuWtdbpUpAGX753jwaGR/Rvb3yJ3tw8V6saKwiWkBZrF1Rpx+F+9i4BmJIj/WEFfB4F/Zn5LrKhPBEotbPUGwCArEO45GINZQENRWLqH446XQpSrKVzQD9+vFXXr2nUsnpG35BeaxdUyVpp0ws9TpcCIIt0DoRVGwzImMzsP5o12rl0uJdwCQCAbEO45GJj3+Cxdyn3fOZ/nlOxz6uPvHKJ06UgD5w39697lwAgWZ39w6rL0EicJIUCPgX9BZwYBwBAFiJccrGGMtrDc9HGXZ26f1eXPnT5YlWVZu5NO/JXwOfV6sYK9i4BmJKu0c6lTJpVXqRDdC4BAJB1CJdcrL4s0R5O51LuiMbi+vT/PKemqmLdcGGT0+Ugj6xdUKWd7f3qG2LvEoDkdA6EVRPM7JcgDeUBvlQDACALES65WG3QL2OkdsKlrHM8PPmerJ8+eUAtnYO69arlKizgf37InLULKmWt9OR+9i4BOLuhSFSD4WjGToobM6u8iLE4AACyEJ9uXczn9aim1M83eFnmq3/Zo5Wf+LOu/cYj+vYDe7W/+7gkqW9oRF+8Z7cuXFilV66oc7hK5JvzGsvlL/Dosb2MxgE4u87+sCRlfCxudnmReo5HdCISy+jzAgCAmcnM2bKYtoayAJ1LWeT+5zv1xXt3a+2CSh0Px/S5Pz6vz/3xeS2rD6qsyKf+EyP6+NUrMnbyDjDGX+DV6nkVLPUGkJTOgbFwKdOdS4kw63DfCS2sKc3ocwMAgOkjXHK5hrIi7e0adLoMJKH16HH93c+f0vL6kH74rjUK+LxqOzaku3cc0Z92dOjJ/T16+8vmaXlDyOlSkafWLqjSl+7drd6hiMqLC50uB4CLdQ4kvtjK9Fhcw+i+yfbeYcIlAACyCOGSy9WXBfRIS7fTZeAsTkRiuvknW2WM0bfevloBn1eSNKeiWO++aL7efdF8DQyPqLiQ/8nBOWsXVCX2Lr3QoytW1jtdDgAXc3IsTpIOc2IcAABZhZ1LLlcXCmggHD3tgmg4z1qrW+/cpuc7+vXl685TY1XxpLcLBnzyehiHg3NeOrcssXeJ0TgAZ9E5EJbPa1RR7Mvo89aFAjJGOkS4BABAViFccrnq0sToSvdg2OFKcDo/frxVdz51SB++fIkuXVrrdDnAaf117xInxgE4s86BYdWU+jO+I7CwgMNMAADIRoRLLlczukiTcMmdtrT26FO/36nLltXqg5ctcroc4KxevqBKz3f0q3co4nQpAFyssz+smlBmR+LGzCov0uFeDjMBACCbEC65XHVpIlzqGuCDoNsc7BnSzT/ZqlnlRfrSm8+Th5E3ZIG1CxN7l554ge4lAKfXOTCsugyfFDdmdnkRO5cAAMgyhEsuR+eSO/Ucj+gd339SkWhc37uhWWUZ3kkBTNdL5pQp4PPosb3sXQJwep0D4YyfFDdmVnlAh3pPyFrryPMDAICpI1xyucqSQhkjdQ0QLrnFUCSqd/9wkw73ntD3bmjW4rqg0yUBSfvr3iXCJQCTC0dj6h0ayfhJcWMayooUjsZ1bGjEkecHAABTR7jkcj6vRxXFhXQuuUQ0FtcHfvqUnm3r1VeuP1/NTZVOlwRM2dr5VXq+Y0CdA+w0AXCqsS+0ah0ai5tVXiRJjMYBAJBFCJeyQHUp4ZIbWGt1653bdN/znfrUNefoVSvrnS4JmJYrz6lXodejf/rls4rFGTsB8GKdY+GSQ2Nxs0fDpUOES64WjcX1739+Xl+8Z7fTpQAAXIBwKQtUl/oZi3OBL92zW7/Y3KYPXbZIb187z+lygGlbXBfUJ163Qg/s7tJ/3MuHAgAv1tk/1rnk1Glxieelc8m9ugfDevv3ntDX79+rP25rd7ocAIALEC5lgZqgX92DnBbnpEdbuvWV+1r0lua5+vtXLnG6HGDG3rqmUW9aPUdfua9F9+484nQ5AFyka3Rk1qmxuMqSQvkLPGrvY3TXjZ5t69XrvvqwnjrQqzkVRXTAAgAkES5lhepSP2NxDvufbe0qLvTqk9eslDHG6XKAGTPG6F//5hydMzukv//F09rffdzpkgC4ROdAWB4jVZU6Ey4ZYzSrvIixOBf61ZY2vfFbj8kYo/9+34VqnlehGKf6AQBEuJQVqkv9GorEdDwcdbqUvGSt1X3Pd2r94moFfF6nywFSJuDz6ptvWy2vx+jmn2zRUIS/YwAkxuKqSv3yepz7MmVWeYCxOBex1uq23+3QP/7yGTXPq9DvPrBO58wuk9fjUTRGuAQAIFzKCtWlhZJE95JDdrb3q71vWJcvr3O6FCDl5lYW6yvXna9dRwZ0y6+3yfINNJD3OgeGHRuJGzOrrEjtvYzFucWOw/364aP79daXNer2d68Z72or8BjG4gAAkgiXskLN6Bs8wiVn/OW5ThkjXbq01ulSgLS4eEmN/uGVS/Tbpw/rjicOOF0OAId1DoQdD5cayot0ZGBYI7G4o3Ug4ckXeiRJH7xskQq8f/344PUaRQmXAAByIFwyxlxpjNlljGkxxnz0NLd5szFmpzFmhzHmp5mu0W2qR78d6hpgqbcT/vJ8p146p3w85ANy0fs3LNKa+ZX6+v0tivJhDshrnQNh1YWcOSluzOzygKyVOljq7QpbWo9pdnmRGsqKXnR5onOJfzMAABkOl4wxXklfl/RqSSskXW+MWXHSbRZLukXSOmvtSkkfzmSNbkTnknO6BsJ65mCvLl9G1xJym8djdONF89XeN6x7n+P0OCBfRWNxdQ8637k0qzwRYrB3yXnWWm1u7VFzU8Up13kMnUsAgIRMdy6tkdRird1nrY1I+rmka066zXslfd1ae0ySrLWdGa7RdSpLCmVMIuhAZt3/fOL//di3hHxw+fI6zS4v0u2PtTpdCgCHHD0ekbVSjcOdS2MdMu10Ljmu7dgJHekPq3neqeFSgccoTrgEAFDmw6XZkg5O+HPb6GUTLZG0xBjziDHmcWPMlZM9kDHmJmPMZmPM5q6urjSV6w4+r0cVxYV0LjngL88f0ayygJY3BJ0uBUg7r8forS9r1KN7j6qlc8DpcgA4oLM/8V7D+c6lRLh1iM4lx21uTexbam6qPOU6di4BAMZkOlya7Ezbk/9FKpC0WNIGSddL+k9jTPkpd7L2O9baZmttc01NTcoLdZvqUsKlTBseiemhPd26bHmtjHHuOGYgk667YK4KvR79mO4lIC91DiQ6hZwOl4oLC1RR7GMszgU27T+moL9AS+pO/aKN0+IAAGMyHS61SZo74c9zJB2e5Da/tdaOWGtfkLRLibApr1WX+tU9yELvTHrihR4NRWK6fBkjccgfVaV+veYlDfrvrYc0GI46XQ6ADOscHcGvdXgsTkrsXSJcct6W/ce0al6FvJ5Tv2jzejyKxq2sJWACgHyX6XBpk6TFxpj5xphCSddJ+t1Jt/mNpP/P3p2Ht1We6R//vlotS15lJyabswdCwpaEnUKBUrqXpSxDV7q3tJ3pOu10uv7ambZTZqYbnW5AoQuFQktnGKDQwkCgkJCwhZDEWewkJLEtr5Jsy5LO7w9ZTiBe5ETWkXTuz3XNVYiN/MBAfHzree/31QDGmAYyx+R2FHTKItRY5VfnUoE9uPkAAa+bMxaF7R5FpKDecUYz0aEkd23ca/coIlJg2WNxjSH7b0g9piagziWb9caH2XKgf8y+JchsLgFoeUlERAoaLlmWlQSuA+4DNgO/syxrkzHma8aYN4982n1AxBjzAvBX4DOWZUUKOWcxymwuKVwqFMuyeHBzO2ctbqDC67Z7HJGCOnluLStmV3PL47v0brSIw7T3D1JX6cXnKfT7j4ebXVuhziWbbWjrBsbuWwJGt5l0NE5ERAr+5GBZ1j2WZS21LGuRZVnfGPm1L1mWdffIH1uWZX3SsqzllmWttCzrt4WesRg1hPzEEyniCR1TKYQtB/rZ2zPAhcfNsHsUkYIzxvDO0+ez9UCUJ3Z22T2OiBRQe/8QM6rsPxIHmWNx/YNJ+geH7R7Fsdbt6sLjMpw097D6U0DhkoiIHGT/21KSk4aQD4DOfvUuFcKDm9sBOP9YhUviTG86cRY1Aa+KvUUcpr1/iBnV9h+Jg0y4BOhonI3Wt3Zz/OwaAr6xt7izx+KS6XQhxxIRkSKkcKlENI7c2tIR1QNWIfzlxXZOmFNTFIWmInYI+NxcsXoO923az4E+/b4j4hQdfYOjzxx2m1Wb+R6so3H2SCTTPLO7Z9y+JdDmkoiIHKRwqUQ0jGMfz7oAACAASURBVBRrdmhzadpFokNsaOvW1pI43ttPbyZlWfz6iTa7RxGRArAsi47oEDOL5I2V7OaSboyzx/Mv9TKUTLNm/vjh0sHNJYVLIiJOp3CpRGTfRVSp9/R7aEsHlgUXHjfT7lFEbNUcDnLu0kZ+/WSbfu8RcYDu+DDDKYsZRbK5NKOqArfLKFyyyfpdmc69Vc1jl3kDuLS5JCIiIxQulYj6oA9joKNfP+BNtwdfPMDMaj/Hz6q2exQR21336sX0DQzz5u8/yrN7euweR0SmUfYIbLEUertdhqbqCvb16GiuHdbv6mZ+uHLCY5IehUsiIjJC4VKJ8Lpd1FX6tD0wzYZTaR7Z2smrl83AGGP3OCK2Wz2/njs+dCbGGC7/8ePc8dQeu0cSkWnSPvIGVrEUekOmd0mdS4VnWRZPtXZPuLUE4HZlfpRQuCQiIgqXSkhDSOHSdNvQ2k3/UJLzljXaPYpI0Vg5p4a7rzuL1c11fPr2Z/jyH59nOKWbgUTKTfvo5lIxhUsBXupVuFRoOztjRGKJCfuWQJ1LIiJykMKlEtIQ8tMZVaH3dHpoawcel+GsxQ12jyJSVMIhP7+89lTed/YCbn68lWt++oTCbpEyM7q5VCTH4iATLu3vHSSt8KKg1rd2A7B6knDp4G1xesNBRMTpFC6VkMYqvzqXptlDWzpY1VxHVYXX7lFEio7H7eKLb1zOf151Es/s6eEff/+c3SOJSB519A9R5fcQ8LntHmXUrJoKhlOWwuwCW7+ri7pKL4saQxN+njaXREQkS+FSCclsLunharoc6Btk874+zls2w+5RRIraW06azXWvXswDmw+wsa3b7nFEJE/a+wdpLKK+JYCmmgAA+3pV6l1I61u7WdVcN2n/ZPa2uGRK4ZKIiNMpXCohDSE/8USKeCJp9yhl6eEtHQDqWxLJwXvOXkA46OPf7t9i9ygikiftfUNF1bcEmb5JgEhMb64VSiQ6xI6OGKvnT1zmDQc3l9KWwiUREadTuFRCsg9Ynf3qXZoOD21tp6m6gmObquweRaTohfwePvLqxaxtifBYS6fd44hIHrT3DxVV3xJk3lgD1DlZQE9l+5aaJ+5bgoOdSzoWJyIiCpdKSOPIu4kdUa2G51syleaRbZ2cu7Rx0hVwEcm45rR5HFNTwXfu34Kld61FSpplWbT3Dxbd5lI4u7mkcKlg/rajC5/bxYrZNZN+rseV+VEipXBJRMTxFC6VkOy7dx3aXMq7DW099A8mdSROZAoqvG4+fsESNrb18ODm9nE/b093XD94iBS5/qEkg8NpZlYX1+ZSpc9Dpc9NRJ2TBTGcSnP3M3s5d1kjFd7Ji93d6lwSEZERCpdKSHZzSaXe+ffQlnY8LsNZSxrsHkWkpFy+ag7zw5X82/1bDrsqPJ22+P6D2zjn23/ly3c/b9OEIpKL9r7Ms8WMIiv0hsz2kp59CuPBze10RhNcfercnD7f486ES3oDQUREFC6VkPqgD2MULk2Hh7Z0cEpzHdUVXrtHESkpXreLf3jNUl7c389/P7dv9Nf7B4f50K1P8d0/b2VOXYBb/9Y22uMhIsWnvT9z5L6xyI7FAYSDfiIxbW0Xwm/XtdFUXcG5S3O7Odc1UiWQ0tFoERHHU7hUQrxuF3WVPjr6FS7lU3vfIC/s69OROJEj9KYTZnFsUxXX37+F4VSa7R1R3vrDtTz4YjtfeuNy/vcTr2JWTQVfuPM5hlNpu8cVkTGMbi4VWaE3ZC40UaH39HupZ4CHt3Zwxeo5o8fdJpO9LS6V1u/tIiJOp3CpxDRoNTzvHtraAcB5Ob5LJyIv53IZPn3RMnZF4nzhzud4yw/W0hMf5tb3nsa1Zy8g5PfwtbesYMuBfn76yA67xxWRV0inLdbt6gKK81hcQ8ivzqUC+N363QC8bXVuR+JAnUsiInLQlMIlY8xyY8w7jDFfMMY0jfzaYmOM7m4vkIaQX+/e5dnDWzqYWe3nuGP0r7HIkbrguBmcPK+W25/aw4KGIHd/7GzOWBQe/fiFy2dy8fFN/OcD22iNxMZ9nancOqcb6kSO3tYD/Vz+48f41RNtvGHlMVT5PXaPdJhwyEdXLHFYr5vkTyptcfv6PZy9uIG59ZU5/3XqXBIRkaycwiVjTMgY8zvgOeBnwNeBWSMf/ibw5ekZT16pscqvzaU8SqbSPLKtg3OXNmJMbivgInI4YwzfufwEPvPaZdz+oTOYXRs47HO+8ubj8bpdfPEPzx8WDCVTaX76fzs44av38/k7n6V3YHjcrzU4nOL6+7dw0tf+zENbxr+lTkTGl/3v6A3fe4SdnTH+/coT+cHfnVyU3wvDQT/JtEXf4Pi/L8jReWRbB3t7Brhqzbwp/XXZY3FJhUsiIo6X6+bS9cCZwIVAFXDok8c9wMV5nkvG0RDyq3Mpjzbu7qFvMMl5y3QkTuRoLZ5RxUdfvXjc66ubair47MXLeGRbJ3c/89Lorz+/t5dLfvQY37hnMwsbgty2bjcXXv8w9zy377AQ6smdXbz+e4/wvb+0APCZO56lS0W/IlOyftfB/47eeMIsHvjkuVxy8pyiDJYgs7kEaHN7Gt22bjf1QR+vWT5zSn+d25X5UUKbSyIikmu4dCnwOcuy/gqkXvGxVqA5r1PJuBpCfuKJFPFE0u5RysJDW9pxuwxnLW6wexQRR7jmtGZOmlvL1/70Avt6B/iXezbzlh+uZV/vID/8u1P4w0fP4u7rzmZGlZ+P/GoDH7jlKfb3DtI7MMzn73yOK/7rcRLJNL+89lR++4HT6Y0P8/k7n9UROZEcdfQP8fafP0Eimebma0/l3688iXCo+HqWDtUwMp82t6dHR/8Qf37hAJedMhufZ2p1rG6jY3EiIpKR68H6ABAZ52NVHB44yTRpyL57159gXrj4ehFKzUNbOjhlXi01Aa/do4g4gttl+OYlK3nTDx7l3G8/RCKV5qo1c/n8646jpjLz3+GK2TX88aNn8fNHd/LvD2zlwusfJuBzE4kO8f5zFvAPr1lKpS/z+9+nLlrKv/zvi/x+w14uXzXnqOfb3zvIgy8e4Ko183K+LUmklPzs0R0MjQS0CxtDdo+Tk+zmUkSbS9Pizg17SKYtrlyTe5F3lludSyIiMiLXdGId8E7g3jE+djnwWN4mkgk1VmXeveuIDjIvnHvhohyuvX+QTS/18ZnXLrN7FBFHWT6rmn+4cAn3PLeff37j8pcVf2d53C4+eO4iXrfiGL509/N0x4f5xbvWsHJOzcs+733nLOQvL7bzlbs3cdqC+ikV0b7S4HCKa29axwv7+kilLd55xvwjfi2RYtQTT3Dr46288YRZJRMsQaZzCSAS0+ZSvlmWxW3rdrNmfh2LZ0z9YhN1LomISFauu69fBC41xjwAvA+wgNcbY24B3oYKvQsmuxre0a93747W83t7AThtQb3Nk4g4z3XnL+GeT5wzZrB0qHnhSm56z6n88aNnHRYsQWYT6rtXnAjAp373zBG/e25ZFv901/O8sK+PxTNCfPveLRzoGzyi1xKZyC8e3cm//u+Ltnztmx7bRSyR4qOvXmTL1z9S9UEfxqhzaTo8ubOLHZ0xrpxikXdWdsMzlU7ncywRESlBOYVLlmU9ClwA+IEfkCn0/iqwELjQsqx10zahvEx2c0m9A0dvR0fmOvRFJfTurYgcbk5dJV998/E8uauLn/zfjtFf7x0Y5g8b9/KhW57iNdc/zJ9fODDua/zqiTZ+v2EPH79gCT9/12qGU2m+cvemQoxfEIPDKd5945N88ndPq7PPRvc+v4+v/fcL/Pjh7dy/af+En2tZFp/63TN89o5nSOcQmqbS1oTdY9GhJDeu3cVrls/k2KbqKc9uJ7fLUF/pI6Jnn7y7bd1uqvweXr+y6Yj+em0uiYhIVs6lPZZlrQXOMcYEgDqgx7Ks+LRNJmM6+O6dHrCO1o7OGLWVXuqCPrtHEZGjdOkps3nwxQNc/+ctWFj8bUcXj7V0kkxbzKjyU1Xh4f2/XM8nLljCJy5YguuQPqUNbd189U+bOG9ZI38/8rGPX7CE79y3hQdeOMCF49yeNJxK0xVLMLO6Iq9/L4PDKYZTaaoq8tcF99U/vcBDWzpwGXjhpT7+6x2raA4H8/b6MrmW9iif+t0znDi3lqHhFF++exNnLm4g5B/7UezWkcAToDkc5KOvXjzua3f0D3HVTx5nQUOQG96+Cq/78PcOb/1bK70Dw1w3wesUs3DIp86lPBscTvE/z+3j8lVzRnvspsrlUueSiIhkTO1KCMCyrAHLsl5SsGQPr9tFXaWPjn6FS0drZ0eMBQ364UqkHBhj+MZbV1JX6ePb926hNRLjvWcv4M6PnMnfPn8B//Pxc7jslDn854PbeP8v19M3OAxkfij/yK0baKqp4D+uPGn0B6UPvGohy2ZW8eW7NxEbOnzTZ3/vIG/78eOc8S8P8s17NjOQmPhei329A7y4v2/Sv4/B4RSX//gxzv/uw+zuys+32d+t281vnmzjI+ct4qb3nMq+3kHe9P1HeWhLe15eXyYXHUrywVvWU+F1c8M1p/DNS1eyv2+Q796/ZczP394R5Rv/8wKvWtrIm0+cxXfv38Ij2zrGfe333PQku7sGeGBzO5/7/eG3Jw4Op/jZIzs4Z0kDJ86tzfvfXyGEg351LuXZzs4YQ8n0pMeTJ+JRuCQiIiPGfZvCGPOLqbyQZVnXHv04kouGkE+bS3mwszPGmYuP/IFKRIpLXdDHXR89i+hgkqUzQxhzcDupwuXm3952AifOreFrf3qBt/5gLT+85hS++qdNdMcT3PmRM6mtPLjF6HW7+OalK7jshse5/s9b+ec3Lh/92LpdXXz41g0MJJK8bsUx/OT/dvC/z+/jm5es5JwljS+baUdHlB8/vJ07N+zFGLjx3ady9pKGMefP9j49v7ePkN/DO3/xJHd86Iyjuib+2T09fPGPz3POkgY+ddEy3C7Dn647mw/csp733LSOT1+0jI+ct+hl/6wkvyzL4jO3P8POzhi3vvc0ZtUGmFUb4O2nNXPzY7u45OTZnDDnYOAznErzyduepsLr5juXn0BVhYct+/v5+G828qePnc2cuoOl9Ylkmg/f+hSb9/Xz03eu4vm9fVz/563MrK7gcxcfO/p5v32yjc5oomS3liCzubTppckDWsndtvYoAItnHHk9gFvH4kREZMREm0srX/F/bwDeDbweWD3yv+8e+fUV0zmkvFxDyK9Sy6MUG0qyv29QfUsiZWZ2bYBlTVVjhiXGGN55xnx+/f7T6Rsc5vXfe4S/7ejim5es5PhZh5eFr2qu55rT5nHj2p08v7cXy7K45fFdXP2Tv1FV4eEPHz2LH15zCrd94HS8Lhfv+PmTfPK2p+mKJXjhpT4++usNXHD9w/zx6Zf4u9PmsagxxAdvWT96mcArHdr7dNN71vBSzwDX3rz+iDuSumIJPnzrBhpDfv7zqpNHfwicF67kzo+cyZtOmMV37tvC+3+5nrUtnQynVMg7HTLh434+d/GxnLn4YLD4mYuX0RDy8/k7nyN5yD/77/+lhWf29PLNS1Yys7qCSp+HH79jFcmUxYdv3cDgcGZLLp22+Nzvn+WRbZ38y6UrOf/YmXzs/MX83WnzuOGh7dy0dieQCaD+6/92cOr8ek5bWLpvqGSeffTGWj61tEdxGY5qi9vjyvwooc0lEREZd3PJsqw12T82xrwJ+A/gEsuyHjvk188Cbgb+33QOKS/XWOXn6d09do9R0nZFMmXeOhYn4jynLqjnTx87m8/e8SwrZ9dw2ao5437uZy8+lvtfOMDn73yOY5uquP2pPVxw7Ayuv/IkagKZTqTTFoa55xPn8MO/tnDDQ9u5b9N+YokUIb+HD527iGvPWkBjlZ/9vYNcdsNjvPvGddz1kTOZW39wA2Ws3qfvX30yH7r1KT76qw385J2rD+vR2dszwE1rdzIwnOKi5U2cvjCMz3PwB71P/HYjHdEh7vjQGdS/oluu0ufhP686iRPm1PBv92/hgc3t1FZ6ufC4mbz2+CbOWdJAhdedr3/kZe+BFw5w18a9LGgIsmhGkMWNVSxsDPLM7h6+de+LvH5lEx941cKX/TXVFV6+8ubj+civNnDTY7t43zkL2dDWzQ//2sKlJ8/m9SuPGf3cBQ1Brr/yJN7/y/V8+Y+b+NblJ/Ct+17kro17+fRFS7li9VwgE6B+/S0r6Ogf4qv//QKNVRX0Dw6zr3eQf73shIL+M8m3hpCP/sEkQ8kUfo/+3cyH7e1R5tVXHtU/z2x9nTaXREQk1/a+fwW+eGiwBJmSb2PMl4BvAXfnezgZW0PIr86lo5S9KU7hkogzHVMT4Jb3njbp59UEvHz5Tcu57tcbeW5vLx+/YMlo+HOoCq+bT120jDeeMIsf/rWFxTNCvOuM+dRUHizlbqqp4OZr13DZDY+/7MjbeL1PFx3fxNffuoJ/uut5vnDnc3z78hMwxrzsqB2Az+Pi1r+1UVXhGQmHZrJxdw+PbOvkW5etfNmRq0MZY3jfOQu55rRmHt7awX2b9nPfpv3c8dQeKn1u3n56M39/4ZIjLvp1ir7BYT73+2cZSqa5d9P+l21weFyGhY0hvn35iWNu071uRRPnHzuD796/lfOWNfLJ256mqbqCr7zl+MM+9zXLZ3Ldqxfzg7+2EIkN8cDmdt5xevNhRd/ukWDymp89wT/c9jS1lV5OmFPDq8Y5jlkqssdDu2IJjqkJ2DxNeWhpjx7VkTjI/D7icRlSaW0+iog4Xa5PjAuB8ZpF48D8vEwjOWkI+YknUsQTST30H6GdnZlwab5uSxKRSbxh5THsvniAZU0hzj927JvjspY1VfG9q08e9+OLZ1Txi3ev5u9++gTX3ryeW957Kh/7zYYxe58Arjmtmfa+If7zwW1UeN10xxPc89w+vG4Xbz+9mfe/aiHhoI9Ht3Vy76b9PLA5s0EDcPWpc7lyzbxJ//4CPjcXr2ji4hVNJJJp/rYjwl0b9452SX3jrSt51dLGSV9nMvdv2s9X//QCH3jVQt515vyjfr1i8cO/ttAVT3D3R89mWVMVrZEY2zuitLRH6egf4tqzF4x7I5wxhq+95Xhec/3/8ZYfrCU+nOI37z+d6nFuCvyH1yzlmT09PLC5nYuPb+Irbz5+zNCqwuvm5+9azeU/fpyW9ihff+uKku/VCo9s30WiCpfyIZlKs7MzxnnHHv1/2y6X0eaSiIjkHC5tAL5ijHnSsqx92V80xswCvgI8NQ2zyTgaQpkHrM7+BPPCCpeOxM7OGLNrAwR8Wq0XkYkZY/jweYvy9nqrmutHj7xd8N2H6egf4rtvO3HM3ieAv79wCe39g9zyt1ZCfg8fPHcR7z17AQ2HFH1fuHwmFy6fSTKV5smdXbywr4+3n9485dl8HhevWtrIq5Y2ctWauXz+zud45y+e5NKTZ/PFNy4/7Hhd3+AwbZE4ixpD4/5+mk5b/MeD2/jeg9uoqvBkbuBLJPnIeeOXS1uWxY7OGAsbgnkNRdJpiz3dA8wLV07+yTlojcS48dFdXHbKHFbOyfz/b8nMKpbMrMr5NebUVfKpi5by//5nMx981UJOn6AXye0y/ODqU/jTsy9x+ao5oz1aY6mt9PGr953GI9s6ec1xE4eipSC7uaTepfxo64qTSKVZMiP3f1fH43EZ0gqXREQcL9dk4gPA/cAuY8xTQDswA1gFRIC3T894MpbGqswDVkd0KG8PyE6zozOmI3EiYptDj7y94/TmCXufsj06r142g9MWhke7nsbicbs4c3HDy4qjj9Qru6Qe2trBu86YT2d0iJb2KC0d0dEj2g0hH9eevYC3n978sq2b3oFhPnnb0zz4YjuXr5rDV998PF+46zm+fe8W4kMpPnXR0sPCo7ZInH/6w3M8sq2TD5+36GW3no1lKJli3c5uzlwUPuy44iv90x+e4zdP7ubD5y3i0yO35x2Nf7nnRTxuw2deu+yoXufasxaw/Jhq1iyon/Rzayq9OQeHM6sruHyCf7dKyegba7rQJC9a8nBTXJZbm0siIkKO4ZJlWZuMMYuAa4E1QBOwBbgVuNGyrIHpG1FeKftutXqXjoxlWezoiPLWk2bbPYqIONg1pzVz1qIG5tVP/iaBx+3iouObCjDVyx3aJfWPdz7Lvz+wlSq/h0UzQpy7tJFFjSGaavzctfElvn3vFm54aDvvPnM+7zlrAZHoEB+45Sl2d8X5+luO5+2nN2OM4forTiLgdfODv7YQT6T45zcehzGGZCrNL9bu5Po/b8XjcnH24gZueGg7M6v8vPusBWPOF08k+cAvn+LRlk7ed/YCvvjG5eP+vdy2ro3fPLmb5cdUc8ND23l+by/fv/rkw44i5urx7RHu3bSfT1+0lJnVFUf0Glkul8lLIFjOsptLEW0u5UVLRyZcWtR49G+0ZTqXFC6JiDhdzmeqLMsaBH40jbNIjrKbS1oNPzKRWIL+waQ2l0TEdvNL5PehZU1V3PnhM+mJD1Nb6T1s2+iSk+fw7J4efvTX7Xz/Ly387JGdGAOVPje/fv/pnHrIRo7bZfjmJSup8Lr5xchtd1efOpcv3PUcz+/t48LjZvL1tx7PjKoKPnTrU6O3nr3hhGNe9jX7Boe59sZ1bGjr5uzFDfzs0Z3MrK7g/a+4lQ3g2T09/PMfN3H24gZuvvZU7nhqN//8h0286QeP8l9vX83yWdUv+/yXega4b9N+uuPDXHvW/MMCqFTa4uv//QKzawO875zDv57kX9Dnxu9xEYlpcykfWtqjNFVXUDVOv9dUuF0ubS6JiEhu4ZIxZtK3VS3LGq/wW/Ks/pBSS5m6bJn3wjy8Wyci4hTGGOqC42/5nDCnlh+/YxXbDvRzw8Pb6Ywm+NZlK8csX3a5DF9+03IqfW5+9NB2fvNkG41Vfn50zSm8bkXTaHh16K1n9UEfZyzK9BF1xxK868YneeGlPr5/deav+dhvNvKNezYzo9rPWw7ZTO2KJfjwrRtoDPn53tUn43YZrlwzj6Uzq/jwrRu49Ia1fPvyEzl+VjX3Pr+f+zft55k9vSN/z/DrJ1r58puO540nHDM61x1P7eaFfX18/+qTqfCqu68QjDE0hPx6Yy1Ptufhprgsj8uQSilcEhFxulw3l6LAZN819HRVIF63i5qAl66YHrCOxM6OkXCpIT8PVSIictCSmVVcf8VJk36eMYbPXnwsjVV+2rri/P0FS6mpfPkWxaG3nn3glvXc/qEzCAf9vP1nT7AzEuO/3rGKC0bKqr97xYlEYkN8+vZnCAf9nL2kgVTa4hO/3UhHdIg7PnTGywrJT55Xx90fO4uP/moDH//NxtFfP3FuLZ+7+Fhee/xMBoZTfP7O5/jYbzZy18a9fP2tK6iu8PCd+7ayqrmON75im0qmV0PIpzfW8sCyLFrao7xt9dy8vJ7bZUhZCpdERJwu13DpWg4Pl+qBi4DlwNfzOZRMrj7o02r4EdreGcXrNsyu01XGIiJ2e884fUpZtZU+br72VC790Vre9YsnCfo87Osd5MZ3r+GsQ3qKKrxufvLO1Vzx48f54C3rue2DZ/C/z+/jkW2dfOuylZwwp/aw155RVcGv3nc6v3x8Fx6X4bUrmg7btLrrI2dx49qdfPf+rVx0/cOcOLeWzugQP3/X6rzeZCeTC4f8tPcP2j1GydvXO0gskcrb5pJbnUsiIkLuhd43jfOhfzfG/Ag4Pm8TSU7qgz66FC4dkZ0dMZrDwaO+JUhERApjdm2Am689lbf9+HHiQyl++d5TWTP/8JvVqiu83PSeU7nshse45mdP0DswzFVr5nLlmnnjvrbP45qwN8ntMrzvnIW89vgmvviH53l4aweXnjybE+ceHlbJ9AoHfWze12f3GCUvnzfFQeZYnDqXREQk50LvCdwJ3Ab8Qx5eS3JUH/Sxu0s1V0diZ2eMhSVSoisiIhnHNlXzp+vOxu0yzJ3ghr2mmgpuvnYNl//4cU6cU8NX3pyf97/m1ldy03vWsL61mxWzavLymjI14ZCfSDSBZVnaGjsK+Q6XMptL6by8loiIlK58hEtrAJX/FFg46OPp3T12j1FyUmmL1kic84+bYfcoIiIyRbnerrd4RhV//dR5BHzuvBZuG2PG3JiSwmgI+Uik0vQNJqkJHP0tZ07V0hGlttJLeIKC/qlwuwxJFXqLiDherrfFfXuMX/YBxwEXAP+Rz6FkcvVBH90xvXs3VXu7B0ik0tpcEhEpcxPdbCelqSHkByASHVK4dBRa2qMsbgzl7flRnUsiIgK5by5dweGF3oPAHuDjwE/yOZRMrj7oI5m29O7dFO3ozKyCL9BNcSIiIiUlHMoEhpFYgoWNNg9Twlrao7z2+Jl5ez2PbosTERFyL/SeP81zyBRlr1PuiiUULk3Bzs4YAAsbtbkkIiJSSsLBg5tLcmS6Ygm6YgkWNebvTTZtLomICIArl08yxnzJGDNrnI8dY4z5Un7HkskcDJf0gDUVOztjVFV48tYzICIiIoXRMLK51BnVbblHKt9l3gAel0udSyIiklu4BHwZmDPOx2aNfFwK6OC7d3rAmorsTXHqqRIRESkt2R4tPfscuekIl7S5JCIikHu4ZDi8cylrDtCdn3EkV/Whg8fiJHc7OmIsUJm3iIhIyfG6XdRWeunUsbgj1tIeJeB1M6smkLfX9LgNyXQ6b68nIiKladzOJWPMu4B3jfypBdxgjOl7xadVACuB+6dnPBlP9lhXROFSzgaHU+ztGeDKxrl2jyIiIiJHoCHkJ6JKgCO2rb2fRTOCuFz52+B2GW0uiYjIxIXecSAy8scG6AW6XvE5CeB/gR/lfzSZSIXXTaXPTbfCpZztimTKvLW5JCIiUprCQZ86l47C9vYopy0M5/U1dVuciIjABOGSZVm3A7cDGGNuBL5uWdaOQg0mk6sP+nQsbgp2dihcEhERKWUNIT8vV5UWJQAAIABJREFU7n/lIr3kIjaU5KXewbz2LUGmc0mF3iIiMtHm0ijLst4z3YPI1NUHfToWNwU7OhUuiYiIlLJwSM8+R2p7R6bMe1FjfsMlj1vH4kREZOLOpW8D37Msa8/IH0/Esizrc/kdTSZTH/TpxpQp2NERY2a1n6A/p0xVREREikw46KcnPsxwKo3Xneu9NALTc1McgNvlUrgkIiITbi69DfgVsGfkjydiAQqXCqw+6GPbgajdY5SMnZ1RFjbk94FKRERECid8yG25M6srbJ6mtGxrj+JxGZrDlXl9XY/LkFS4JCLieBN1Li0Y64+leISDPt2YMgU7O2O8buUxdo8hIiIiR6hhJFzqjA4pXJqilvYoCxqCed/40m1xIiICoH3iElYf9DM4nCaeSNo9StHrjiXojg+zUH1LIiIiJash5AdQLcAR2N4ezfuROBi5LU7hkoiI403UufT6qbyQZVn3HP04MhXh4MHV8EqfeoQmojJvERGR0hfOhkva3J6SRDJNa1ecN5yQ/w1ut1vH4kREZOLOpf8m06VkcngdC3DnZSLJWf0h4dKcuvyeny83O0fCpYV5viFFRERECifbuaTNpanZFYmRSlvTuLmUzvvriohIaZkoXFLPUpGrGwmXdCXv5HZ2Zkos59QF7B5FREREjlCV34PP7aJT4dKUZG+KWzQNb7K5VegtIiJMXOjdWshBZOpGj8XpAWtSOzpizKuv1LXFIiIiJcwYQzjkIxLVsbip2LK/H2OmJ1xS55KIiMDEm0uHMcZcBJwKHAPsA56wLOvP0zGYTK7+kOt4ZWJbD/RPyyq4iIiIFFY45KNT4dKUPLOnh6Uzqgj48t9i4VK4JCIi5HhbnDFmljHmCeBe4DrgnJH/vc8Y86QxZvY0zijjqPJ78LqNjsVNYiiZYlckzrKmKrtHERERkaPUEPLr2WcKLMvimd09nDS3dlpeX5tLIiICOYZLwE/IbCudbVlWk2VZJ1iW1UQmZGoC/mu6BpTxGWOoD/ro0o0pE9rRkSmxXDJT4ZKIiEipCwf9KvSegtZInO74MCfNm55wye1ykUxbWJYCJhERJ8s1XDof+KxlWY8d+ouWZa0F/hF4db4Hk9zUB/06FjeJrQf6AVg6U8fiRERESl3DyLE4hRm52bi7G4CTpylc8rgyF0treUlExNlyDZcOAAPjfGwA6MzPODJV4aBP4dIkth7ox+MyLGxQuCQiIlLqwiEfQ8k0sUTK7lFKwtNtPQR9bpbMmJ4NbvdIuJRMp6fl9UVEpDTkGi59E/iaMWbOob848udfBr6R78EkN3UKlya19UCU+Q1BfB7dFCciIlLqwkE/gG6My9HTu3tYOadmNATKt+zrqndJRMTZcr0t7iIgDGw3xmwA2oEZwCkjf3yhMebCkc+1LMu6Mu+TypjCQZ9KLSex9UA/K2bV2D2GiIiI5EF45LbczmiC5nDQ5mmK2+Bwihf29fG+cxZO29fwKFwSERFyD5cagG0j/wdQDQwC2Q6mxjzPJTmqD/roH0ySSKa1mTOGgUSKtq44l5ysCw1FRETKQUMos7nUqc2lSW16qY/hlDVtN8WBNpdERCQjp3DJsiwVdhep+mDm3bvueIKZ1RU2T1N8WtqjWBYs1U1xIiIiZSEbLunGuMk9vbsHgJOnMVzyjHYuKVwSEXEyrbqUuPBIuKQHrLEdvClO4ZKIiEg5qB999tHm0mQ2tnUzuzbAjGl8A9Ltyvw4oc0lERFny/VYHMaYWcCbgNnAYd+hLMv6bB7nkhxlH7BU6j22re39+Nwu5ocr7R5FRERE8sDncVFd4VHnZA6e3t0zrUfiQJtLIiKSkVO4ZIy5CrgZMEAH8Mrv5hagcMkG2VLLrrgesMaydX8/CxuDeNxa0hMRESkXDSG/Opcm0dE/xJ7uAd51xvxp/TqukXAprXBJRMTRct1c+gbwe+BDlmX1TeM8MkV1lSPhkh6wxrT1QJRVzXV2jyEiIiJ5FA75VAkwidG+pXnaXBIRkemX6zpHGPi5gqXiU1vpwxgdixtLdCjJ3p4Bls4M2T2KiIiI5FE46CcS0xtrE3l6dzcel2HF7Jpp/ToHb4tLT+vXERGR4pZruHQncN40ziFHyO0y1FX61Dswhm0q8xYRESlLDVU+OrW5NKGNbT0cd0w1FV73tH4dbS6JiAjkfizuOuDnxpifAX8Bel75CZZl3ZPPwSR39UGfNpfGsO1AFFC4JCIiUm7CQT/d8QTJVFq9imNIpS2e3dPLJSfPnvavld1cSqYULomIOFmu4dJS4FRgAXDtGB+3gOl9W0TGVR/U5tJYthzop8LrYm69booTEREpJw0hH5YF3fFhGqv8do9TdLZ3RIkOJaf9pjgAjzt7LE7hkoiIk+UaLt0I9AFvAFo4/LY4sVE46GNbe9TuMYrO1gP9LJ4RGn1HTURERMpDOJQJlCKxIYVLY9jY1g1Mf5k3gMuMhEuWwiURESebyubSpZZl3Tedw8iRqQv66Nbm0mG2HujnrEUNdo8hIiIieRYOZm7L1Y1xY3t6dw81AS8LGoLT/rU8rsyxRG0uiYg4W66H1J8E5k3nIHLkwkEf3fEEaX1TH9U7MMyBviGWNqlvSUREpNxkN5c6o7oxbiwb23o4cW4txkz/9rY6l0REBHLfXPokcJMxZoDxC73j+RxMclcf9JG2oGdgmPqRd/Kc7uBNcSGbJxEREZF8awhlnnd0Y9zhYkNJth7o57XHNxXk66lzSUREIPdw6amR/715gs9RobdNsoFSV2xI4dKILaPhkjaXREREyk1NwIvHZYhoc+kwz+7pJW3BSQXoW4JDNpfS6YJ8PRERKU65hkvXkrkRbjxKNGwUDo6UWkYTLJ5h8zBFYtuBKEGfm9m1AbtHERERkTwzxhAO+dS5NIaNuzNl3ifNKVC4ZLS5JCIiOYZLlmXd9MpfM5lD3K8GrgYuBX6a18kkZwc3l/SAlbVlfz+LZ1YVpGtARERECi8c9BOJaXPplZ5u62FBQ5C6Am2zZzeXFC6JiDhbrptLo4wxp5EJlK4AZgJdwG/zPJdMQXikdyCicGnUtvZ+zj9Wa1wiIiLlKhzyqXNpDM/s6eHMAt6Wq84lERGBHMMlY8wKMoHSVcB8IEHmKNwngR9alpWcrgFlcrWVXkCbS1mR6BCd0YT6lkRERMpYQ8jPrkjM7jGKSncswYG+IZYfU12wr+kZ7VxSuCQi4mSu8T5gjFlojPmCMeY54Bng08Bm4J3AEsAAGxUs2c/vcVPl9yhcGrH1QBRQmbeIiEg5CwfVufRK29ozz0BLCnhbrtuV+XFCm0siIs420eZSC5kS7yeADwK/tyyrG8AYU1OA2WQK6kM+hUsjtrXrpjgREZFy11DlJ55IEU8kqfRNuemhLGWfgZYU8BlIm0siIgITbC4BrWS2k1YA5wFnGmP0nbtI1QcVLmVt2d9PVYWHmdV+u0cRERGRaRIeKazW9tJB2dtyZ9VUFOxrukYLvdMF+5oiIlJ8xg2XLMtaAJwF3AxcAPwJOGCM+enIn+vtiSISDvpU6D1i24Eoy3RTnIiISFlrCGXeROqM6sa4rG3thb8t1zMaLhXsS4qISBGaaHMJy7IetyzrY8Bs4LXAH4HLgDtGPuX9xpjV0zui5CKzuaSHK8uy2HKgv6Dr4CIiIlJ4o7flanNp1LYDUZbMKFzfEoBbm0siIsIk4VKWZVlpy7L+bFnWtUATcClwO3AJ8IQxZvM0zig5qA/66YolsCxnL5R19A/ROzDM0gIWWYqIiEjhhUc2lyJ6cw2A3vgw7f1DBQ+X1LkkIiKQY7h0KMuyEpZl/cGyrKuAmWRuj2vJ+2QyJfVBL8Mpi/4hZ1/etysSB2Bho8IlERGRcpbtXOrU5hJwaJm3XZtLCpdERJxsyuHSoSzLilmW9SvLst6Ur4HkyNQHM+/edTu8d6k1EgOgub7S5klERERkOlV43VT5PToWN2JbexSAJTMKWw3gcWV+nNDmkoiIsx1VuCTFY/TGFIeHS21dcVwGZtUG7B5FREREplk45FOh94htB6IEvG5mF/gZaCRb0uaSiIjDKVwqE/Uj4VKXw9+9a43EmVUbwOfRv9oiIiLlLhzyq3NpxLb2fhbPCOFyFfa23OzmksIlERFn00/gZWI0XHL45lJrV5zmsI7EiYiIOEE46NOxuBF23BQHkM2ydCxORMTZFC6VidHreB0eLrVFYsyrD9o9hoiIiBRAOORXoTfQNzjM/r5BlswsbN8SgDEGj8uQSqcL/rVFRKR4KFwqE5U+DxVeF10OXg3vGxymOz6szSURERGHaAj56IoNkXb41kzLaJm3Pbflul1Gm0siIg6ncKmM1Ff6HL251BaJA7opTkRExCnCQR9pC3oGhu0exVYtB0bCpZn2hEselyGVUrgkIuJkCpfKSH3IR7eDw6XWkXBpnjaXREREHKGhyg9AxOE3xm090E+F18WcOnuegVwuQ8pSuCQi4mQKl8pIfdDv6ELv1q4YAM1hdS6JiIg4QTiYCZc6HB4ubWuPsqgxhLvAN8VlZTqXFC6JiDiZwqUyEg7qWFw46CPk99g9ioiIiBRAQ/ZCE4eXere023NTXJbb5VLnkoiIwylcKiN1lToWpyNxIiIizhEO6VhcdCjJ3p4BW26Ky1LnkoiIKFwqI7WVXmKJFMMpZ14F29YVV5m3iIiIg9QGvLgMjt7ctvumONBtcSIionCprNRWegHodeCNKYlkmn29A8xTuCQiIuIYLpehPuin08HH4rYd6AewdXPJ7TKk0s58c1NERDIULpWRmkAmXOqJOy9c2tMdJ23BPJV5i4iIOEpDyOfoY3Et7VF8Hhdz6wK2zeBxGXQqTkTE2RQulZHaykypZe+A8969a+2KA9CsziURERFHaQj56XRwuLT1QD8LG4J43PY91mtzSUREFC6VkVoHby61RUbCJR2LExERcZRwyNm35W5rj7LUxiNxMNK5pNUlERFHK3i4ZIy52BizxRjTYoz5xwk+73JjjGWMWV3I+UpZtnPJieFSayROwOumscpv9ygiIiJSQOGgn4hDO5fiiSR7ugdsLfMG8LgNKRV6i4g4WkHDJWOMG/gh8DpgOXC1MWb5GJ9XBXwceKKQ85W62kDmWFx33HkPWG1dMebVV2KMsXsUERERKaBwyEd0KMngcMruUQpue3sMgCUz7Q2X3C6XbosTEXG4Qm8unQq0WJa1w7KsBPBb4C1jfN7XgW8Dg4UcrtRVVXhwGWfeFtcaiTNPfUsiIiKO0xDKvLnmxKNxW4vgpjgAt4G0pXBJRMTJCh0uzQZ2H/Lne0Z+bZQx5mRgrmVZ/z3RCxljPmCMWW+MWd/R0ZH/SUuQy2WoCXgddywunbZo64qrb0lERMSBwsHMkXgn3hi3rT2K121sfwbyuFzqXBIRcbhCh0tjnVka/U5kjHEB/w58arIXsizrJ5ZlrbYsa3VjY2MeRyxttZU+ehy2udTeP8RQMq2b4kRERByooSobLjlvc6mlvZ+FDSFbb4qD7G1xCpdERJys0N+J9gBzD/nzOcBLh/x5FbACeMgYsws4Hbhbpd65y2wuOevhqjWS6RuYFw7aPImIiIgUWjiYORbX4dDNpcU29y1BptA7mU7bPYaIiNio0OHSOmCJMWaBMcYHXAXcnf2gZVm9lmU1WJY137Ks+cDfgDdblrW+wHOWrNpKr+M6l1q74gC2r4SLiIhI4YWznUsO21waSKRo64qzdIa9fUugzSURESlwuGRZVhK4DrgP2Az8zrKsTcaYrxlj3lzIWcpVrQM7l9oicVwGZtcF7B5FRERECqzS56HS53Zc59L2jiiWZf9NcQAel9FtcSIiDucp9Be0LOse4J5X/NqXxvnc8woxUzmprfQ57lhcW1ecWbUBvDb3DYiIiIg9wiGf426LG70pbob94ZLLaHNJRMTp9NN4makJeOkbTDrqG3xrV1xl3iIiIg4WDvrpdNjm0rpdXVT5PSxosL9z0uNWuCQi4nQKl8pMbaUXgD4H9S61RWLMq7f/wUpERETs0RDyO65z6bHtEU5bGLb9pjgAt8ulcElExOHs/24keZUNl3ocEi71DQ7THR/W5pKIiIiDNYR8RGLO2Vza0x2nNRLnzEVhu0cB1LkkIiIKl8pObSBzY4pTepfaIropTkRExOnCIR+RaIK0QwKOx1oiAJy1uMHmSTJ0W5yIiChcKjM1Dttcah0Jl+Zpc0lERMSxwkE/ybRF36Aznn/Wbu+kIeRnaRHcFAfgNoZkOm33GCIiYiOFS2WmNpAJl3rjzni4au2KAdAcVueSiIiIU4VDmc3tTgf0LlmWxWPbI5y5KIwxxu5xAHC7DSllSyIijqZwqczUVjrvWFw46CPk99g9ioiIiNikIeQHIOKAG+O2tUfp6B/irMXF0bcEmc6llDaXREQcTeFSmamuyIQsTjoWpyNxIiIizpbdXIrEyv/NtbUtnQCcuag4+pYg07mkQm8REWdTuFRmPG4XVRUeehxyLK6tK64ybxEREYdz0ubS2pYI8+ormVtEzz8eFXqLiDiewqUyVFvppdcBm0tDyRQv9Q4wT31LIiIijlZX6cMY6CjzzqVkKs0TOyJFdSQOwO1yaXNJRMThFC6VodqAzxGdS3u6B7AsmFdE79yJiIhI4bldhvpKX9lvLj23t5f+oWRRHYkDcLvQ5pKIiMMpXCpDtZVeR3QutXXFAWhW55KIiIjjhUM+ImW+ufTY9ggAZywqvs2lVNrCshQwiYg4lcKlMlQT8NLrgM6ltshIuKTNJREREccLB/1EYuW9ufTY9k6Obaoa7ZgqFh6XAUDLSyIizqVwqQw5ZXNpZ2eMgNdNY1VxPWCJiIhI4ZX75tLgcIr1u7qL7kgcZI4lAiTTaZsnERERuyhcKkPZzqV0mb999MyeHlbMrsYYY/coIiIiYrOGkJ/OMu5c2tDazVAyXXRl3nBwc0m9SyIizqVwqQzVVnpJWxBNJO0eZdoMDqd4fm8vq5rr7R5FREREikBDyEffYJJEsjy3Z9Zu78TtMpy6oPiefQ5uLilcEhFxKoVLZagm4AUo696lZ3b3MJyyWDO/zu5RREREpAjUBzPH5Lti5Xk0bm1LhBPn1FBV4bV7lMNkw6Vy35oXEZHxKVwqQ7WVPgB6yjhcWt/aDcCqZoVLIiIicvDNtb7B8nv+6Rsc5tk9PZy1uPj6luDgsThtLomIOJfCpTJUW5l5uOqOl+c7dwDrd3WxZEZoNEgTERERZ6sOeADoK8NLTZ7Y0UXaoijLvAHcrsyPFOpcEhFxLoVLZahuJFwq1xvj0mmLp1q7Wa0jcSIiIjJitBagDJ9/1rZ0UuF1cUpzrd2jjEmbSyIionCpDNUEMts8vWW6ubStPUrfYFJl3iIiIjKquqJ8j8WtbelkdXM9fo/b7lHGlO1cSqUULomIOJXCpTKUfeeuXDuX1u3qAlCZt4iIiIyqLtMLTXZ3xdnWHuW8ZY12jzKug7fFledNfSIiMjmFS2XI53ER9LnL9ljcU63dNIT8zKuvtHsUERERKRLVFSOdS4NJmyfJr7+82A7ABcfNtHmS8Y3eFmdpc0lExKkULpWp2kpfWW8urZlfhzHG7lFERESkSHjcmTfXyq3Q+4HNB1jYEGRBQ9DuUcalziUREVG4VKZqAl56B8qvc+lA3yB7ugdY1awjcSIiIvJy1QFvWRV6R4eSPLGjiwuOm2H3KBMaPRanziUREcdSuFSmaiu9Zbm5tH5XNwBr5qvMW0RERF6uJuAtq0LvR7d1kEilOf/Y4j0SB+BxjxR6a3NJRMSxFC6VqdpKb1l2Lq3b1UXA62b5rGq7RxEREZEiU11RXptLD25up7rCw+oiv8TE7cr8SKFjcSIizqVwqUzVBMqzc+mp1m5OmluL161/dUVEROTlqgNe+gbKo9A7nbb465Z2zl02o+ife9xGm0siIk5X3N+p5IjVVmY6l6wyurUjNpTkhX19Rf/unYiIiNijOuApm2Nxz+zpoTOa4MIi71uCg51LCpdERJxL4VKZqg14GU5ZxBMpu0fJm6d395BKW6xW35KIiIiMoZyOxf3lxXbcLsO5SxvtHmVS6lwSERGFS2WqttILUFa9S+t2dWEMnDyv1u5RREREpAjVBLxEh5KkyyDkeGBzO6ua66it9Nk9yqRGb4tLp22eRERE7KJwqUzVBDIPIj3xhM2T5M9Trd0c21RNdYXX7lFERESkCFUHvFgW9A+Wdu/SSz0DbN7XxwXHFv+ROACPjsWJiDiewqUyld1c6i2TUu9kKs2G1m5WN6tvSURERMZWXeEBKPnepQdfbAfgguNm2jxJbg5uLilcEhFxKoVLZarcjsW9uL+fWCKlMm8REREZV01g5M21En/++cvmAzSHK1nUGLR7lJxkw6VyOI4oIiJHRuFSmaodPRZX2g9XWet3dQGozFtERETGVT0SLvWVcLgUTyRZuz3CBcfOxBhj9zg58WhzSUTE8RQulamDm0vl0bm0vrWbY2oqmF0bsHsUERERKVLZzaVSPha3tiVCIpnmguNKo28JwO3K/EihziUREedSuFSmKrxu/B5XWXQuWZbF+l3d2loSERGRCVWXwbG4BzcfoMrvYU0JPfdoc0lERBQulbHaSm9ZHIvriiXY3zfIiXNq7B5FREREithoofdAad4Wl05b/OXFdl61tBGfp3Qe092jt8WlbZ5ERETsUjrftWTKagO+sjgW19oVB2BBQ2mUWoqIiIg9Qn4PLlO6x+Kef6mX9v6hkjoSB9pcEhERhUtlraZMNpfaIplwqTlcafMkIiIiUsyMMVQHvCV7LO7ODXvxuV2cf2xphUsu3RYnIuJ4CpfKWG0JP1wdqjUSxxiYU6dwSURERCZWE/CW5G1xg8Mp7tywh4tXNFFb6bN7nCnR5pKIiChcKmPl0rnU2hWjqbqCCq/b7lFERESkyFVXlOaba/c+v5++wSRXrZlr9yhTdrBzSeGSiIhTKVwqY7WV5dG51BaJM69eW0siIiIyueqAh77B0iv0/u26NprDlZy+MGz3KFPmcWV+pNDmkoiIcylcKmM1AS+Dw2kGh1N2j3JUWrvi6lsSERGRnJTisbidnTH+tqOLK1bPHe0vKiXaXBIREYVLZay20gtQ0kfj4okkHf1DNId1U5yIiIhMrhSPxd22bjdul+Ftq+bYPcoRyYZLyZTCJRERp1K4VMbqRsogS/loXFtX5qa4uToWJyIiIjmoDnjpGyydcGk4leaOp/Zw/rEzmFFdYfc4RyS7bJWyFC6JiDiVwqUyVhso/c2l1kgmXGpWuCQiIiI5yNYCDCVLoxbgwc3tdEaHSrLIO8sYg8dlSKXTdo8iIiI2UbhUxmrK4Fjc7pHNJXUuiYiISC6qKzwA9A2URqn3bevaaKqu4NyljXaPclTcLqNCbxERB1O4VMZqR47F9ZbwsbjWSJzqCs/o34uIiIjIRKpHNrdL4WjcSz0DPLy1g7etnoPHXdqP5R6XIaXOJRERxyrt72IyobI4FtcVV5m3iIiI5CwbLpVCqfft6/eQtuCK1aV7JC5Lm0siIs6mcKmMVfrceN2GnhJ4uBpPWyTGPB2JExERkRxVV4xsLhX5808qbfG79bs5Z0lDWVxc4nYZUgqXREQcS+FSGTPGUBPwlezmUjKVZk/3gMq8RUREJGc1o8fiirtz6dGWTvb2DHBlCRd5H8rtcum2OBERB1O4VOZqK70l27m0r3eQZNpSmbeIiIjkrDqQKfQu9mNxt6/fTV2ll9csn2n3KHmhziUREWdTuFTmagPekt1cao1kboqbV6/OJREREclNqRyLa2mPsnp+PX6P2+5R8kKdSyIizqZwqczVVpZwuNQVA9DmkoiIiOSswuvG73EVfbjUGU3QECqf23A9bkMqnbZ7DBERsYnCpTJXE/AV/Vr4eNoicXxuFzOrK+weRUREREpIdcBL32DxPv+k0xbd8QT1wfIJl7S5JCLibAqXylxmc6k0O5daI3Hm1Adwu4zdo4iIiEgJqQl46Rso3kLvvsFhUmmLcNBv9yh54zaGtAq9RUQcS+FSmasNeIklUiSSpbem3NoV101xIiIiMmXVFZ6i3tzujGbe+AuX0bE4t8uQVKG3iIhjKVwqc7WVmVLLYn7AGotlWbRFYjSHVeYtIiIiU1Psx+K6YiPhUhltLmU6lxQuiYg4lcKlMldTmXlHrHegtI7GdcUSxBIp5mlzSURERKaoJuAt6jfWItEhgDLrXHKpc0lExMEULpW52kBmc6nUboxr7YoDuilOREREpq66wlvUt8VFYuV3LM7j0uaSiIiTKVwqc9ljcaUWLrVFFC6JiIjIkakJeOkbTGIVacF09lhcXWX5hEtuY0imS6/jU0RE8kPhUpmrDWQeWrpL7Ma41kgcY2BOncIlERERmZrqgIdU2iKWSNk9ypgi0SGqKzz4POXzKO52GZQtiYg4V/l8R5Mx1QVLc3OptStGU3UFFV633aOIiIhIiamuyDz/FOvRuEgsQUOofMq8IVPorc0lERHnUrhU5kJ+Dx6XoavENpfaInGVeYuIiMgRqQkU9225kWiirMq8IbO5pM4lERHnUrhU5owx1AV99JRYuNTapXBJREREjkx1oLg3l7pi5RcueVxGt8WJiDiYwiUHqKv0jhZHloJ4IklH/5DKvEVEROSIjB6LG0zaPMnYIrEhwmV2LE6bSyIizqZwyQHqKn10l1DnUltX5qa4eeGgzZOIiIhIKSrmY3HptEV3fJhwmW0uubW5JCLiaAqXHKA+6KO7hDaXWiOZcKlZx+JERETkCFQHPEBxHovrHRgmlbYIh8otXHKRVrgkIuJYCpccoLbSR3cJdS61ZcMlHYsTERGRI1BVUbybS5HYEIA6l0REpKwoXHKA+qCX7vgwllUa3/DbuuJUV3iorSyvhy4REREpDLfLUOX30DdYhOFSNPOGXzhi2JQPAAAgAElEQVSoziURESkfCpccoK7SRyptFW2p5Su1dsVpVt+SiIiIHIXqgJe+geJ79omMVBWU27G4zOZS2u4xRETEJgqXHKBuZAOop0SOxrVFYszTkTgRERE5CtUBb5Eei8tuLpVXuKTNJRERZ1O45AB1wUzvQFcJlHonU2n2dA+ozFtERESOSnVFsR6Ly3Qu1SlcEhGRMqJwyQGym0ulUOq9r3eQZNpinsIlEREROQo1AW9R3hbXFUtQE/DidZfXY7hbhd4iIo5WXt/VZEzZ20i6Y8X3gPVKrSM3xelYnIiIiByN6iINlyKxRNkdiYNM55I2l0REnEvhkgPUltDmUmtXDECF3iIiInJUqiu8RXmZSSQ6VHZl3gBul0ubSyIiDqZwyQGqKzy4XaYkwqWXegZwuwxN1RV2jyIiIiIlrCbgJTqUJJkqrhvMumKJ0a3ycqLNJRERZ1O45ADGGOoqvXSVwLG4rliCukofbpexexQREREpYdUBDwD9Rba9FIkmCIf8do+Rd9lCb8tSwCQi4kQKlxyirtJHTwlsLnVGEzSU4aq4iIiIFFZ1Rea23N4i6l1KpS264+XZuZR9Y1DLSyIizqRwySHqgj66YsUfLpXrqriIiMj/b+/+gyO/7/qOv967K2m1kna1+nGS7k62E2yfff5RHC5OKNCcIZTYdOIyuIwzCeTXJDNtk06BUgJ0khDaaZ20k5ZpaDCQGBhKIC6kDrGdTp04gYT8cGrisy+xOWzi+yHdnVa6lXZ12pV2P/3ju6uTZZ2k29v9fr+3n+djxoN+rPb7nvCd00ev7/vz/iBcuf4gXFpciU+4dG65qrpTV651muHSWj1e2xABAOEgXPJEPtNzRcxcCoZcdl+rOAAACFe2GS6dj8+2uOaDvm5c66Qa4RJzlwDAT4RLnhgZ6NXCcnye3F1Mtx7PCwAAwtXsXIrTtrhCM1zqwrXOhc4lwiUA8BHhkieGM71aKFdjPWSxslbT0spaV7aKAwCAcDUHesdpW1yh1Oxc6r61znrnUi2+a00AQOcQLnliJNOrtbpTqRKf1vDNFhqn2XXjggsAAIQrjgO958sVSd0+c4lwCQB8RLjkiXxjEdMMcOKo0FhwdWOrOAAACFemN6lUwrQYo3BprtG5NJLpvrVOMhH8WVGPcZc8AKBzCJc8kc8ET+/mYzzU+0KrePcNuQQAAOEyM2X7e2K1LW6+XNVwpkepZPctwVN0LgGA17rvNxu2tN65FONwqXmCSje2igMAgPBl0ykVY3RaXKFc6dp1TpKZSwDgNcIlT+QzzW1x8Q2X5krBtrixATqXAADA5cv198RqW1yhVO3adU4q2excqkdcCQAgCoRLnmju7V9Yjs8Ca7P5clWphK2f7gIAAHA5sv09MRvoXe3+ziW2xQGAlwiXPDGUTilh8e5cmi9XlR/olZlFXQoAAOgCcZu5VChXu/ZU3KQxcwkAfEa45IlEwpTP9MZ65tJcqcpJcQAAoG2y6R4txmTmUq3utLDcvWsdOpcAwG+ESx7JD8Q7XJovV7r2aR4AAAhftj+lxfOrci76wGNhuSrnuvfgkubMJcIlAPAT4ZJH8pme9RPZ4qhQrmq0S4dcAgCA8OX6e1St1VVZi37IdHMNNjrYnWudZCL4s4JtcQDgJ8Ilj+QzvToX54Hepe4dcgkAAMKXTfdIUixOjCuUGuFSl651UmyLAwCvES55JJ/pjW3nUmWtpqXKWtcuuAAAQPhy/UG4FIcT4wrliqRu7lxqDvSOvksMABA+wiWP5AeCzqU4zB3YrNtbxQEAQPiyjXApDifGNdc63dql3QyXyJYAwE+ESx4ZGQjmDpSrtahLeZlmq3i3LrgAAED4sumUpHh0Ls011jr5TE/ElXQGnUsA4DfCJY8MZ4LgZiGGW+MKjZrGOC0OAAC0SXNb3OL5tYgrCU7FzWd6lEp25/KbmUsA4Lfu/O2GLY00w6Xl+IVL8405BHQuAQCAdonTtrhClx9ccqFziXAJAHxEuOSR/ECwwIrjUO8LJ6gwcwkAALRH87S4YgxOyy2Uq109WzKVCP6soHMJAPxEuOSRfKNz6VwMFlibFcpVpRKmbH8q6lIAAECX6E0l1N+T1LkYzFyaL1e7+lTc5m4/OpcAwE+ESx5ptmLHsXNpvtEqbmZRlwIAALrInmyfzixVoi5DhVKly7fFBX9W1AmXAMBLhEseyaZ7lDDpXAxnLhXKla5uFQcAANGYyKZ1urgSaQ1rtbrOnV/t6rVOiplLAOA1wiWPJBKm4Uyv5mMZLnV3qzgAAIjGRDat00vRhksLy6tyTl291kmunxZXj7gSAEAUCJc8M5zp0UI5+rkDmxVKVY0Odu+CCwAARGMy26fZ4oqci66jpjmSoJvXOnQuAYDfCJc8M5Lp1UIMO5fmy919PC8AAIjGRDatylpdxQiHehdKwcynbl7rXOhcIlwCAB8RLnlmONMbu4HeK6s1lSprXd0qDgAAojGZS0uSZhej2xpXaHYuDXTvzKVmuLRWI1wCAB8RLnlmZKBH55bjtS3uQqt49y64AABANCayQbh0ejG6E+N82BbXDJfqEW4/BABEJ/RwyczeYGbPmtkxM3vfFt//BTM7amZPmdljZnZ12DV2s/xAMNA7yrkDmzUXXN3cKg4AAKIx2QyXIjwxrlCqyEzKZ7p3rZNKBH9WMHMJAPwUarhkZklJH5N0p6SDkt5kZgc3vexJSYecc7dKelDSh8OssdvlM72qrtW1XK1FXcq6ucYcgrEufpoHAACisScbdEZHvS0un+ld7+7pRsxcAgC/hd25dLukY865551zVUmfknT3xhc4577onFtufPo1SftDrrGrjTSemMVpqPeFziW2xQEAgPbqSyU1MtAbbbhU6v6DS1LMXAIAr4UdLu2TdHzD5ycaX7uYd0p6pKMVeWY40yNJWijHZ+5SocS2OAAA0Dl7hvp0JsJwab5c7fqDSxIJk5lUq9ejLgUAEIGww6WteoG3fLxhZm+RdEjSRy7y/Xeb2RNm9sTZs2fbWGJ3awY4cepcKpSr6kmasulU1KUAAIAuNJlLR9q5NFeudPUw76akmWoxmusJAAhP2OHSCUnTGz7fL+nU5heZ2esl/ZqkNzrntjzawzl3v3PukHPu0Pj4eEeK7Ub5GIZL8+WKRgZ6Zda9cwgAAEB0JrNpzRajPS3Ohw7tZMIY6A0Ango7XPqmpOvM7BVm1ivpXkkPbXyBmd0m6bcVBEtnQq6v6zVPKVkoxydcKpSqGmXeEgAA6JCJbFqFckWrtfC3bK3V6jq3vOrFWieVMNWYuQQAXgo1XHLOrUl6j6TPS/qOpD91zj1jZh8yszc2XvYRSYOSPm1mf2NmD13k7dCCXH+PzKT55RjNXCpXvWgVBwAA0ZjMpeWcdHYp/O6l+Ua3uA9rHTqXAMBfoQ+5cc49LOnhTV97/4aPXx92TT5JJky5/p54dS6VK7p6NBN1GQAAoEtNZIOuodnFFe0d7g/12s1Tcb3oXEomVCNcAgAvhb0tDjEwkumN18wltsUBAIAOmsimJUmni+EP9T6zGHRLjdG5BADoYoRLHsoPxCdcWlmtqVytedEqDgAAojHZCJeiODFuthFohd0xFYWkmeqESwDgJcIlD+UzPVoox2PmUmG9VZxwCQAAdMbIQK96kqbTi+HPXJpphEt7st3fpU3nEgD4i3DJQ/kYbYubLwV1+HA8LwAAiIaZac9QWqej6FxaPK+xwV71pZKhXztsqaSpVg//RD4AQPQIlzyUH+hdHy4Ztbly8ARxdLD7n+YBAIDoTObS61vUwjRTXNFkLh36daNA5xIA+ItwyUP5TK8qa3Wdr9aiLmW9c4ltcQAAoJMmsxF1LhVXNJnt/nlLkpRKGKfFAYCnCJc8lM/0SJLmY7A1rtDoXBphoDcAAOigiYjCpVPnzmvKk86lhNG5BAC+IlzyUL7RJbQQg61xhXJVvcmEhvpSUZcCAAC62ES2T+VqTUsr4R1qUq6saXFlTVPDfoRLqSSnxQGArwiXPNQcnh2Hod7zpapGBnplZlGXAgAAulhz7lGY3UuzjWv50rmUTCToXAIATxEueai5LW5hObwndxdTKFc1ypY4AADQYRPZIOCZLVZCu2ZzgDgzlwAA3Y5wyUP5TLy2xY0wzBsAAHTYZDb8zqWZom+dS6a1ej3qMgAAESBc8lCuvzHQOw7hUqnCSXEAAKDj1juXwtwWVzwv6cKWvG5H5xIA+ItwyUOpZEK5/h6di8PMpXJVo4N9UZcBAAC6XH9vUtl0KtTOpVPFFY0M9CrdkwztmlFKEi4BgLcIlzw1MtCr+YhnLp2v1rRcrbEtDgAAhGIyl16fgxSG2eLK+nY8HxAuAYC/CJc8NZyJvnOpUA4Gao4x0BsAAIRgIpvW6aXwBnrPFFe8mbckBdviOC0OAPxEuOSpkUyvCqVow6XmzKeRAbbFAQCAzpvMpnU61M6l897MW5LoXAIAnxEueWpPtk9nQnxyt5VmuMW2OAAAEIaJbFpnS5VQApCV1ZoWllc961xK0LkEAJ4iXPLUZLZfc6WKKmu1yGooNDqX2BYHAADCMJFLq1Z3mit1/gFbc7bTZK6/49eKCzqXAMBfhEuemhoOnqKdWYyue6nQWNjRuQQAAMLQHK4dxolxp4rnJUl7PepcIlwCAH8RLnmq2aI9E+Lcgc3my1X1phIa7EtFVgMAAPBHM1wK48S4C51LhEsAgO5HuOSpC+HS+chqKJSrGh3olZlFVgMAAPDHRDY4RCSMzqUZD8Ol4LS4etRlAAAiQLjkqeb+/zCe3F1MoVTRKPOWAABASEYH+5RMmGZDCJdmiyvK9fco0+tPhzadSwDgL8IlTw32pTTUl4p8W9zIQF9k1wcAAH5JJkx7hvp0OoSZkzPFFa9OipOanUuESwDgI8Ilj00NpyPdFjdXCrbFAQAAhGUimw5lW9zs4nnvwqVEwlSrES4BgI8Ilzw2meuPdFtc0LlEuAQAAMIzke0LZf0zc25lfQyBL1IJU80RLgGAjwiXPDaVTUe2LW65uqbzqzWNDbItDgAAhGcym+74zKWV1ZoK5ap3nUvJRIJtcQDgKcIlj03m0jpbqmi1Fv6pHnNLVUnSGAO9AQBAiCZyaS2trGm5utaxa5xpzHTy6aQ4qdG5RLgEAF4iXPLYVC4t56QzS50farnZXDm4Jp1LAAAgTJPZIPDp5FDv5kxL/zqXgnDJsTUOALxDuOSx5tO0mXPhD/UulILOpVE6lwAAQIgmGuFSJ+cuNbfd+RYupRImSXQvAYCHCJc8tnc4GDIZxdylQil4WjhK5xIAAAjRxHrnUufWP6fOBe/t20DvRCNcYu4SAPiHcMljzc6lKE6Mm2uGS5wWBwAAQtRc/3QyXJotntdQOqXBvlTHrhFHzc6lOtviAMA7hEseG+pLaaA3GUnn0lypqqG+lNI9ydCvDQAA/DXYF4Q+nTwxbqa44t2WOCmYuSTRuQQAPiJc8piZaTKX1uxiBDOXylXmLQEAgEhMZPs627m0uOLdljhpw8ylGuESAPiGcMlzU7n+9bkAYZpbqnBSHAAAiMRENt3RsQAzxRVNZT3sXEoGf1rQuQQA/iFc8txUrrOLq4splCt0LgEAgEhMZtM6vVjpyHtX1+qaK1U0NexfuMRpcQDgL8Ilz03l0jqztKK1Wj3U6xZKVU6KAwAAkZhorH/qHQhBTi+uyDn5OXPJGuESA70BwDuES56bzPWr7qSzpc48vdvKWq2u+eUq2+IAAEAk9g73a7XmdGap/euf5qBwH2cuJZm5BADeIlzyXPOpWpgnxi0sr8o5aYxtcQAAIALT+SD4Ob6w3Pb3bq6pfOxcSiWbp8WF2xEPAIge4ZLnJpvhUohDvQvl4Cnh6ACdSwAAIHzTIxlJ0vH59odLs8XgFN5JD8OlJDOXAMBbhEueu9C5dD60axZKVUl0LgEAgGjsGw46l04stH/9M1Nc0UBvUkN9qba/d9w1B3pzWhwA+IdwyXO5/h719yRDPTFurjHfiYHeAAAgCumepCayfR3qXFrR1HC/rDHc2icJo3MJAHxFuOQ5M9NULq2ZxTDDJTqXAABAtPbnMx2ZuXSquOLlvCXpwswlwiUA8A/hEjSZS4fauVQoVZRKmHL9PaFdEwAAYKPpfL+Oz7d/W9xs8bwms36GS8lE8KcF2+IAwD+ESwg9XJorVTQ62OtluzgAAIiH6ZGMZhdXtFZr38lmq7W6zixV/O1cYqA3AHiLcAmayqU1u7gS2kKgUKpyUhwAAIjUdD6jWt1ppo0P2M4uVeScNJnrb9t7XkmS6wO92xfYAQCuDIRL0FSuX7W6Wx+03Wlz5apGmbcEAAAitH8kCIDaOdS7GVRNDdO5BADwC+ES1lu32/nkbjtzSxWNc1IcAACI0HQ+I0ltHerdHDPg67a4xHrnEuESAPiGcAmabCyAZovtH2q5mXNOhXKFziUAABCpqVxayYS1daj3TGMtNZX1c1tcs3OpTrgEAN4hXIKmGnMBwuhcWq7WtLJa1yidSwAAIEKpZEJTubROtLFzaaa4ov6epLL9qba955UkSecSAHiLcAnKZ3rUm0qEEi4VSlVJ0hjhEgAAiNh0PqPjC+3rXDp17rymcmlvT8RNJYI/LZi5BAD+IVyCzExTuXQo4dLZxtBwtsUBAICo7c/3t3Wg99GZRR2YHGrb+11p6FwCAH8RLkFSMHcgjJlLhUa4NDZA5xIAAIjW9EhGZ5YqWlmtXfZ7FZdX9b3Csm7Zn2tDZVemC6fF1SOuBAAQNsIlSArmLoWyLa7c2BY3ROcSAACI1vRIMHfyRBu2xh05WZQk3bpv+LLf60qVXA+XIi4EABA6wiVICk6MO7240vHTPeaWgs6lkQHCJQAAEK3pfEaS2jLU+6mT5yRJt+zzt3MpSecSAHiLcAmSgm1xqzWnuXKlo9cplKsaSqfUl0p29DoAAAA7mR4JwqV2DPU+cqKoq0czymV6Lvu9rlQpZi4BgLcIlyBJmsymJUmzHd4aN1eqaJyT4gAAQAyMD/apN5XQiTYM9T5ysqibPe5akjZ2LhEuAYBvCJcgSdo7HMwc6PTcpblShZPiAABALCQSpv3D/Tp+mdvi5stVnVg4r1s9D5dSieBPi7Ua4RIA+IZwCZKCmUtS5zuXCqWqRjkpDgAAxMT+kYyOz1/etrjmMG+fT4qTpGSSziUA8BXhEiRJI5le9SYTHe9cKpSrnBQHAABiYzrff9kDvY+cCIZ5e78tzhrhkiNcAgDfEC5BUtAWPpHr00zx8gdaXsxara6FZTqXAABAfOzPZ7SwvKpSZa3l9zhysqhXjg0om/Z3mLfEzCUA8BnhEtZNZfs72rk0v1yVc9IYM5cAAEBMTI8EcyePX8ZQ7yMnGOYtbTgtjplLAOAdwiWsm8ylOzpzqVCqSpJGOS0OAADExHQ+I6n1cOnsUkWniiu61fN5S1LQCW8m1er1qEsBAISMcAnrpoaDcMl1aJ98M1waI1wCAAAxMT3SCJcWWhsN8HRzmDedS5KC7qU1tsUBgHcIl7BuKptWtVbXfLnakfefK1UkSaNsiwMAADGRz/RooDfZ8lDvp04UZSbdRLgkSUqYMXMJADxEuIR1k7lg5kCn5i41w6UxBnoDAICYMDPtz2d0fL61zqUjJ4v6vvFBDfal2lzZlSmVIFwCAB8RLmHd/nwQLn2vcHnH8V5MoVxVT9KU7WfxBQAA4mN6pL/lzqUjJ8+xJW6DJNviAMBLhEtYd+2eQSUTpu/MLHbk/eeWKhod6JOZdeT9AQAAWhF0Li1f8tzJ04srOr1YIVzaIJVM0LkEAB4iXMK6dE9S144P6plTxY68f6FcZd4SAACInemRjMrVms4tr17Szx05EayZOCnuAjqXAMBPhEt4iZv2ZvXMqc50LhVKFU6KAwAAsdMcDXD8ErfGPXWyqIRJB/dmO1HWFSmYuVSPugwAQMgIl/ASB/dmdWaporNLlba/91yJziUAABA/0/mMJF3yUO+nTxZ17Z5BZXqZJ9mUMDqXAMBHhEt4ieaTt6NtnrvknNMcnUsAACCGpkcuvXPJOaenThR1y77hTpV1RUolTXXCJQDwDuESXuKmqWBmQLvnLpWrNVXW6hqjcwkAAMTMULpHw5keHZ/ffbg0u7iiuVKFeUubMHMJAPxEuISXyGV6tD/fr6NtnrtUKAXb7EYH6FwCAADxM53P6MTC7rfFPdUY5n0L4dJLBDOXCJcAwDeES3iZg1PZtodLc81wic4lAAAQQ/vz/Ze0Le7IiaKSCdPBKYZ5b5RMJOhcAgAPES7hZW7am9MLhbLKlbW2vedcqSpJzFwCAACxND0SdC7tdl7QkZNFXbdnUOmeZIcru7LQuQQAfiJcwsvctDcr56Tvzrave6lAuAQAAGJsOt+v6lpdZ0s7n5jrnNORk0XmLW0hQbgEAF4iXMLLNE+Me6aNW+Oa2+JGBtgWBwAA4mf/SEaSdjXU+7HvnNF8uapb93NS3GZ0LgGAnwiX8DJTubTymR49c7KdnUsVZdMp9aa45QAAQPxM5/slace5S0++uKD3/vGTunlfVj91274wSruiBKfF1aMuAwAQMv7Sx8uYmW7am9PRmTZ2LpWrGhtiSxwAAIin/fmMhtIpfeTRZ3WkcRLcZs+fLekdD3xT40N9+uTbbtdAXyrkKuOPziUA8BPhErZ0cG9Wz84uabXWnidPhVJFYwOESwAAIJ7SPUn98bteKzPTT3/8q3rwWyde8v0ziyv6uU98Qwkz/cE7btc4D822FHQuES4BgG8Il7Clm/ZmVa3V9XdnS215v7lSVaODzFsCAADxdfO+nB56zw/p0NV5/ZtPf1sf+N9Pa7VW19LKqt72yW9qvlzVJ9/+al0zNhB1qbGVpHMJALxELy+2dHCqMdT75KJumMxe9vsVShX94CtHL/t9AAAAOml0sE9/8I7bdd+j39Xv/OUL+s7MklJJ03Onl/S7bz3EEO8dsC0OAPxE5xK29MrxQaV7Em05MW6tVtfC8iqdSwAA4IqQSib0az95UP/t3u/XUyfP6at/V9B9P32rDh/YE3VpsUfnEgD4ic4lbCmZMN0wmdXRma0HWl6K+XJVUvAkEAAA4Epx9/fv08GprE4snNcdNxAs7UYqkWDmEgB4iM4lXNTBvVkdPbUo5y5vgTBXCsKlcTqXAADAFea6iSGCpUtA5xIA+IlwCRd1096sFlfWdGLh/GW9T6FckUTnEgAAQLdLJUxr9facNgwAuHIQLuGibtqbk6TLnrs0V2qESwN0LgEAAHSzRMJUq9G5BAC+IVzCRR2YGFLCpKOnLm/u0hN/v6D+nqT2Dve3qTIAAADEUSphql3mSAUAwJWHcAkX1d+b1PeND15W51Kt7vT5Z2b1ozfuUbon2cbqAAAAEDfMXAIAPxEuYVs37c3q6Ezr4dI3XpjXXKmqu26eamNVAAAAiKNg5hLhEgD4hnAJ2zq4N6uZ4ormy9WWfv6Rp2eU7knojhvG21wZAAAA4iaZSDBzCQA8RLiEbV0Y6n3pc5dqdadHnp7VHQf2KNObandpAAAAiJlUks4lAPAR4RK2dXAqK0k62sLcpW99b0Fnlyq66xa2xAEAAPggYQz0BgAfES5hW/mBXu3NpfV0C+HSw0dm1JdK6Edv2NOBygAAABA3KQZ6A4CXCJewo9uuzuvrzxfkLuEpVL3u9MjTMzp8YFwDfWyJAwAA8EHztLhLWTcCAK58hEvY0euuH9eZpcolnRr3/15c0OlFtsQBAAD4JJUwSaJ7CQA8Q7iEHR2+Pjjp7fFnz+76Zz53ZEa9bIkDAADwSjIZhEsM9QYAvxAuYUd7smkdnMrqS7sMl+p1p0efntU/um5cQ+meDlcHAACAuKBzCQD8RLiEXTl8YFzfenFBxfOrO772yePnNFNc0U/eOhlCZQAAAIiLhDXCJWYuAYBXCJewK4cP7FGt7vSVY3M7vvaRIzPqTSb0YzdOhFAZAAAA4mK9c6lGuAQAPiFcwq686qphDaVTevzZM9u+zjmnR56e1Y9cN6YsW+IAAAC8kkwGf14wcwkA/EK4hF1JJRP6kevG9KXnzm57tOy3TxR18tx53ckpcQAAAN5h5hIA+IlwCbt2+Po9Or1Y0Xdmli76moePzKgnafpxtsQBAAB4J5lonhZXj7gSAECYCJewa687MC5Jevy5rbfGLVfX9JknT+qHrh1TLsOWOAAAAN8kjc4lAPAR4RJ2bSKb1o1TWT3+7Nktv3//l5/XmaWK/uUd14ZcGQAAAOIglSRcAgAfES7hkhw+MK5vfW9BiyurL/n6TPG8Pv6lv9NP3jqlV18zElF1AAAAiFKSmUsA4CXCJVySw9ePq1Z3+srfzr3k6x9+9FnVnfS+N9wQUWUAAACIWmp95hLhEgD4hHAJl+RVV+c11Jd6yda4J19c0J8/eVLv+pFXaHokE2F1AAAAiFIyEfx5QecSAPglFXUBuLL0JBP64evG9KXnzsq5YNHwob84qvGhPv3zw8xaAgAA8BmdSwDgJzqXcMkOHxjX7OKKvju7pIe+fUpPvnhOv/QTBzTYR1YJAADgs8T6zKV6xJUAAMIUerhkZm8ws2fN7JiZvW+L7/eZ2Z80vv91M7sm7Bqxvdddv0eS9OjTs7rvke/qpr1Z3fOq/RFXBQAAgKil1sOliAsBAIQq1HDJzJKSPibpTkkHJb3JzA5uetk7JS04566V9FFJ94VZI3Y2mUvrxqmsPvbFYzpVXNH7/8nB9adUAAAA8FdyfVsc6RIA+CTsfUy3SzrmnHtekszsU5LulnR0w2vulvTBxscPSvrvZmauOeAHsXD4wLi+M7Oou26Z1GteORp1OQAAAIiBZufS/V9+Xp/99qmIqwGA6LzvzhuV6++JuozQhB0u7ZN0fMPnJyS95mKvcc6tmVlR0qikuY0vMrN3S8zCLQkAAAvYSURBVHq3JF111VWdqhcX8U+/f5/+6m/n9Ct33hh1KQAAAIiJq0YzeuX4gI6eWtTRU4tRlwMAkfmFHz8QdQmhCjtc2mrv1OaOpN28Rs65+yXdL0mHDh2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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*8/1000\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "#plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "#plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "#plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "#plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "plt.plot(x, X[C4, :][0], label='Cat. Q')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C4\", fontsize=20)\n",
    "plt.ylabel(\"Amplitude\", fontsize=15)\n",
    "plt.xlabel(\"Time (ms)\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*8/1000\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "plt.plot(x, X[C4, :][0], label='Cat. Q')\n",
    "plt.legend()\n",
    "plt.title(\"1-beat ECG for every category\", fontsize=20)\n",
    "plt.ylabel(\"Amplitude\", fontsize=15)\n",
    "plt.xlabel(\"Time (ms)\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据增强\n",
    "\n",
    "为了使所有类别的数据都处于相同的水平，所以需要进行数据增强处理。然而，这里只是将最少的类（第 3 类）增加到与类别 1 相同的级别。由此我们将能够具有大约 5x800 观测值的测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 伸展\n",
    "def stretch(x):\n",
    "    # 将 187 的长度随机重新采样\n",
    "    l = int(187 * (1 + (random.random()-0.5)/3))\n",
    "    y = resample(x, l)\n",
    "    # 长度小于 187 的填充 0\n",
    "    if l < 187:\n",
    "        y_ = np.zeros(shape=(187,))\n",
    "        y_[:l] = y\n",
    "    # 长度大于 187 的裁剪到 187 的长度\n",
    "    else:\n",
    "        y_ = y[:187]\n",
    "    return y_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\python36\\lib\\site-packages\\scipy\\signal\\signaltools.py:2223: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  Y[sl] = X[sl]\n",
      "d:\\anaconda3\\envs\\python36\\lib\\site-packages\\scipy\\signal\\signaltools.py:2225: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  Y[sl] = X[sl]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[0, :], 'r') # X[0, :] == X[0]\n",
    "plt.plot(stretch(X[0, :]), 'b')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 放大\n",
    "def amplify(x):\n",
    "    alpha = (random.random()-0.5)\n",
    "    factor = -alpha*x + (1+alpha) # 放大的幅度几乎很小\n",
    "    return x*factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\python36\\lib\\site-packages\\scipy\\signal\\signaltools.py:2230: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  Y[sl] += X[sl]  # add the component of X at N/2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[0, :], 'r') # X[0, :] == X[0]\n",
    "plt.plot(stretch(X[0, :]), 'y')\n",
    "plt.plot(amplify(X[0, :]), 'b')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def augment(x):\n",
    "    \n",
    "    result = np.zeros(shape=(4, 187))\n",
    "    for i in range(3):\n",
    "        if random.random() < 0.33:\n",
    "            new_y = stretch(x)\n",
    "        elif random.random() < 0.66:\n",
    "            new_y = amplify(x)\n",
    "        else:\n",
    "            new_y = stretch(x)\n",
    "            new_y = amplify(new_y)\n",
    "        result[i, :] = new_y\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "examples for C0:  90589\n",
      "examples for C1:  2779\n",
      "examples for C2:  7236\n",
      "examples for C3:  803\n",
      "examples for C4:  8039\n"
     ]
    }
   ],
   "source": [
    "print(\"examples for C0: \", X[C0, :].shape[0])\n",
    "print(\"examples for C1: \", X[C1, :].shape[0])\n",
    "print(\"examples for C2: \", X[C2, :].shape[0])\n",
    "print(\"examples for C3: \", X[C3, :].shape[0])\n",
    "print(\"examples for C4: \", X[C4, :].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将 augment 这个函数按照所设定的 axis 应用\n",
    "result = np.apply_along_axis(augment, axis=1, arr=X[C3]).reshape(-1, 187)\n",
    "\n",
    "# 将 C3 的数据增强到与其他一样的级别\n",
    "classes = np.ones(shape=(result.shape[0],),dtype=int)*3\n",
    "\n",
    "# 按垂直方向堆叠扩增的数据\n",
    "X = np.vstack([X, result]) \n",
    "y = np.hstack([y, classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机从各个类别中抽取 800 个索引，用于后面建立测试集\n",
    "\n",
    "subC0 = np.random.choice(C0, 800)\n",
    "subC1 = np.random.choice(C1, 800)\n",
    "subC2 = np.random.choice(C2, 800)\n",
    "subC3 = np.random.choice(C3, 800)\n",
    "subC4 = np.random.choice(C4, 800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test set shape:  (4000, 187)\n",
      "test examples:  4000\n"
     ]
    }
   ],
   "source": [
    "X_test = np.vstack([X[subC0], X[subC1], X[subC2], X[subC3], X[subC4]])\n",
    "y_test = np.hstack([y[subC0], y[subC1], y[subC2], y[subC3], y[subC4]])\n",
    "print(\"test set shape: \", X_test.shape)\n",
    "print(\"test examples: \", X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train examples:  109150\n"
     ]
    }
   ],
   "source": [
    "# 除去测试集的数据，剩下的都是训练集\n",
    "\n",
    "X_train = np.delete(X, [subC0, subC1, subC2, subC3, subC4], axis=0)\n",
    "y_train = np.delete(y, [subC0, subC1, subC2, subC3, subC4], axis=0)\n",
    "print(\"train examples: \", y_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = shuffle(X_train, y_train, random_state=0)\n",
    "X_test, y_test = shuffle(X_test, y_test, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (109150, 187, 1)\n",
      "y_train (109150,)\n",
      "X_test (4000, 187, 1)\n",
      "y_test (4000,)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.expand_dims(X_train, 2)\n",
    "X_test = np.expand_dims(X_test, 2)\n",
    "\n",
    "print(\"X_train\", X_train.shape)\n",
    "print(\"y_train\", y_train.shape)\n",
    "print(\"X_test\", X_test.shape)\n",
    "print(\"y_test\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\python36\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:368: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 转换为 one-hot 向量\n",
    "\n",
    "ohe = OneHotEncoder()\n",
    "y_train = ohe.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ohe.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (109150, 187, 1)\n",
      "y_train (109150, 5)\n",
      "X_test (4000, 187, 1)\n",
      "y_test (4000, 5)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train\", X_train.shape)\n",
    "print(\"y_train\", y_train.shape)\n",
    "print(\"X_test\", X_test.shape)\n",
    "print(\"y_test\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![论文提出的模型结构](https://ai2-s2-public.s3-us-west-2.amazonaws.com/figures/2017-08-08/0efe20fd1dc65030fa57f7c0727f13b93d51adce/1-Figure1-1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_obs, feature, depth = X_train.shape\n",
    "batch_size = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 187, 1)       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 183, 32)      192         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 183, 32)      5152        conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 183, 32)      0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 183, 32)      5152        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 183, 32)      0           conv1d_3[0][0]                   \n",
      "                                                                 conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 183, 32)      0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 90, 32)       0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 90, 32)       5152        max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 90, 32)       0           conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 90, 32)       5152        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 90, 32)       0           conv1d_7[0][0]                   \n",
      "                                                                 max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 90, 32)       0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 43, 32)       0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 43, 32)       5152        max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 43, 32)       0           conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 43, 32)       5152        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 43, 32)       0           conv1d_9[0][0]                   \n",
      "                                                                 max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 43, 32)       0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 20, 32)       0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 20, 32)       5152        max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 20, 32)       0           conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 20, 32)       5152        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 20, 32)       0           conv1d_11[0][0]                  \n",
      "                                                                 max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 20, 32)       0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 8, 32)        0           activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 256)          0           max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 32)           8224        flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 32)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 32)           1056        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 5)            165         dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "softmax_1 (Softmax)             (None, 5)            0           dense_3[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 50,853\n",
      "Trainable params: 50,853\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "\n",
    "# Input\n",
    "inp = Input(shape=(feature, depth))\n",
    "# Conv\n",
    "C = Conv1D(filters=32, kernel_size=5, strides=1)(inp)\n",
    "\n",
    "# 【1】\n",
    "# Conv\n",
    "C11 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(C)\n",
    "# ReLU\n",
    "A11 = Activation(\"relu\")(C11)\n",
    "# Conv\n",
    "C12 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A11)\n",
    "# Shortcut\n",
    "S11 = Add()([C12, C]) \n",
    "# ReLU\n",
    "A12 = Activation(\"relu\")(S11)\n",
    "# Pool\n",
    "M11 = MaxPooling1D(pool_size=5, strides=2)(A12)\n",
    "\n",
    "#【2】\n",
    "# Conv\n",
    "C21 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(M11)\n",
    "# ReLU\n",
    "A21 = Activation(\"relu\")(C21)\n",
    "# Conv\n",
    "C22 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A21)\n",
    "# Shortcut\n",
    "S21 = Add()([C22, M11])\n",
    "# ReLU\n",
    "A22 = Activation(\"relu\")(S11)\n",
    "# Pool\n",
    "M21 = MaxPooling1D(pool_size=5, strides=2)(A22)\n",
    "\n",
    "\n",
    "# 【3】\n",
    "# Conv\n",
    "C31 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(M21)\n",
    "# ReLU\n",
    "A31 = Activation(\"relu\")(C31)\n",
    "# Conv\n",
    "C32 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A31)\n",
    "# Shortcut\n",
    "S31 = Add()([C32, M21])\n",
    "# ReLU\n",
    "A32 = Activation(\"relu\")(S31)\n",
    "# Pool\n",
    "M31 = MaxPooling1D(pool_size=5, strides=2)(A32)\n",
    "\n",
    "# 【4】\n",
    "# Conv\n",
    "C41 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(M31)\n",
    "# ReLU\n",
    "A41 = Activation(\"relu\")(C41)\n",
    "# Conv\n",
    "C42 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A41)\n",
    "# Shortcut\n",
    "S41 = Add()([C42, M31])\n",
    "# ReLU\n",
    "A42 = Activation(\"relu\")(S41)\n",
    "# Pool\n",
    "M41 = MaxPooling1D(pool_size=5, strides=2)(A42)\n",
    "\n",
    "# 【5】\n",
    "# Conv\n",
    "C51 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(M41)\n",
    "# ReLU\n",
    "A51 = Activation(\"relu\")(C51)\n",
    "# Conv\n",
    "C52 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A51)\n",
    "# Shortcut\n",
    "S51 = Add()([C52, M41])\n",
    "# ReLU\n",
    "A52 = Activation(\"relu\")(S51)\n",
    "# Pool\n",
    "M51 = MaxPooling1D(pool_size=5, strides=2)(A52)\n",
    "\n",
    "F1 = Flatten()(M51)\n",
    "\n",
    "# FC\n",
    "D1 = Dense(32)(F1)\n",
    "# ReLU\n",
    "A6 = Activation(\"relu\")(D1)\n",
    "# FC\n",
    "D2 = Dense(32)(A6)\n",
    "D3 = Dense(5)(D2)\n",
    "# Softmax\n",
    "A7 = Softmax()(D3)\n",
    "\n",
    "model = Model(inputs=inp, outputs=A7)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exp_decay(epoch):\n",
    "    initial_lrate = 0.001\n",
    "    k = 0.75\n",
    "    t = n_obs//(10000 * batch_size)  # every epoch we do n_obs/batch_size iteration\n",
    "    lrate = initial_lrate * math.exp(-k*t)\n",
    "    return lrate\n",
    "\n",
    "lrate = LearningRateScheduler(exp_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = Adam(lr = 0.001, beta_1 = 0.9, beta_2 = 0.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 109150 samples, validate on 4000 samples\n",
      "Epoch 1/75\n",
      "109150/109150 [==============================] - 6s 57us/step - loss: 0.3050 - acc: 0.9076 - val_loss: 1.0323 - val_acc: 0.6795\n",
      "Epoch 2/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.1419 - acc: 0.9614 - val_loss: 0.5477 - val_acc: 0.8432\n",
      "Epoch 3/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.1034 - acc: 0.9719 - val_loss: 0.5406 - val_acc: 0.8435\n",
      "Epoch 4/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0859 - acc: 0.9759 - val_loss: 0.3033 - val_acc: 0.8987\n",
      "Epoch 5/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0739 - acc: 0.9794 - val_loss: 0.3508 - val_acc: 0.8915\n",
      "Epoch 6/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0650 - acc: 0.9811 - val_loss: 0.4919 - val_acc: 0.8725\n",
      "Epoch 7/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0583 - acc: 0.9827 - val_loss: 0.3856 - val_acc: 0.8793\n",
      "Epoch 8/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0542 - acc: 0.9837 - val_loss: 0.2867 - val_acc: 0.9085\n",
      "Epoch 9/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0512 - acc: 0.9847 - val_loss: 0.2678 - val_acc: 0.9218\n",
      "Epoch 10/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0462 - acc: 0.9856 - val_loss: 0.2792 - val_acc: 0.9122\n",
      "Epoch 11/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0424 - acc: 0.9870 - val_loss: 0.2032 - val_acc: 0.9365\n",
      "Epoch 12/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0405 - acc: 0.9874 - val_loss: 0.2302 - val_acc: 0.9243\n",
      "Epoch 13/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0376 - acc: 0.9883 - val_loss: 0.2026 - val_acc: 0.9368\n",
      "Epoch 14/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0372 - acc: 0.9881 - val_loss: 0.2321 - val_acc: 0.9217\n",
      "Epoch 15/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0339 - acc: 0.9890 - val_loss: 0.2597 - val_acc: 0.9310\n",
      "Epoch 16/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0343 - acc: 0.9889 - val_loss: 0.2346 - val_acc: 0.9358\n",
      "Epoch 17/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0295 - acc: 0.9903 - val_loss: 0.2951 - val_acc: 0.9238\n",
      "Epoch 18/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0291 - acc: 0.9907 - val_loss: 0.3181 - val_acc: 0.9147\n",
      "Epoch 19/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0279 - acc: 0.9909 - val_loss: 0.2312 - val_acc: 0.9350\n",
      "Epoch 20/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0258 - acc: 0.9911 - val_loss: 0.1955 - val_acc: 0.9443\n",
      "Epoch 21/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0256 - acc: 0.9913 - val_loss: 0.2694 - val_acc: 0.9315\n",
      "Epoch 22/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0247 - acc: 0.9919 - val_loss: 0.2180 - val_acc: 0.9460\n",
      "Epoch 23/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0248 - acc: 0.9918 - val_loss: 0.2074 - val_acc: 0.9395\n",
      "Epoch 24/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0218 - acc: 0.9927 - val_loss: 0.1966 - val_acc: 0.9408\n",
      "Epoch 25/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0203 - acc: 0.9930 - val_loss: 0.2366 - val_acc: 0.9405\n",
      "Epoch 26/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0210 - acc: 0.9929 - val_loss: 0.2185 - val_acc: 0.9465\n",
      "Epoch 27/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0211 - acc: 0.9927 - val_loss: 0.2238 - val_acc: 0.9455\n",
      "Epoch 28/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0202 - acc: 0.9933 - val_loss: 0.2317 - val_acc: 0.9418\n",
      "Epoch 29/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0203 - acc: 0.9930 - val_loss: 0.2678 - val_acc: 0.9310\n",
      "Epoch 30/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0181 - acc: 0.9934 - val_loss: 0.2829 - val_acc: 0.9365\n",
      "Epoch 31/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0175 - acc: 0.9939 - val_loss: 0.2659 - val_acc: 0.9265\n",
      "Epoch 32/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0168 - acc: 0.9942 - val_loss: 0.3896 - val_acc: 0.9062\n",
      "Epoch 33/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0192 - acc: 0.9934 - val_loss: 0.4019 - val_acc: 0.9052\n",
      "Epoch 34/75\n",
      "109150/109150 [==============================] - 6s 50us/step - loss: 0.0156 - acc: 0.9946 - val_loss: 0.1812 - val_acc: 0.9465\n",
      "Epoch 35/75\n",
      "109150/109150 [==============================] - 5s 50us/step - loss: 0.0171 - acc: 0.9942 - val_loss: 0.2954 - val_acc: 0.9368\n",
      "Epoch 36/75\n",
      "109150/109150 [==============================] - 5s 50us/step - loss: 0.0141 - acc: 0.9950 - val_loss: 0.2316 - val_acc: 0.9460\n",
      "Epoch 37/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0176 - acc: 0.9939 - val_loss: 0.2132 - val_acc: 0.9513\n",
      "Epoch 38/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0137 - acc: 0.9951 - val_loss: 0.2711 - val_acc: 0.9372\n",
      "Epoch 39/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0127 - acc: 0.9957 - val_loss: 0.2861 - val_acc: 0.9380\n",
      "Epoch 40/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0150 - acc: 0.9948 - val_loss: 0.2057 - val_acc: 0.9532\n",
      "Epoch 41/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0134 - acc: 0.9953 - val_loss: 0.2840 - val_acc: 0.9355\n",
      "Epoch 42/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0152 - acc: 0.9948 - val_loss: 0.2361 - val_acc: 0.9462\n",
      "Epoch 43/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0120 - acc: 0.9958 - val_loss: 0.3130 - val_acc: 0.9352\n",
      "Epoch 44/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0136 - acc: 0.9951 - val_loss: 0.3158 - val_acc: 0.9362\n",
      "Epoch 45/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0133 - acc: 0.9952 - val_loss: 0.2693 - val_acc: 0.9515\n",
      "Epoch 46/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0132 - acc: 0.9954 - val_loss: 0.2304 - val_acc: 0.9482\n",
      "Epoch 47/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0129 - acc: 0.9955 - val_loss: 0.2237 - val_acc: 0.9463\n",
      "Epoch 48/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0106 - acc: 0.9962 - val_loss: 0.2970 - val_acc: 0.9370\n",
      "Epoch 49/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0115 - acc: 0.9959 - val_loss: 0.2830 - val_acc: 0.9425\n",
      "Epoch 50/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0109 - acc: 0.9962 - val_loss: 0.3800 - val_acc: 0.9143\n",
      "Epoch 51/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0106 - acc: 0.9963 - val_loss: 0.3226 - val_acc: 0.9415\n",
      "Epoch 52/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0131 - acc: 0.9954 - val_loss: 0.2299 - val_acc: 0.9463\n",
      "Epoch 53/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0106 - acc: 0.9962 - val_loss: 0.3794 - val_acc: 0.9210\n",
      "Epoch 54/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0114 - acc: 0.9962 - val_loss: 0.2862 - val_acc: 0.9455\n",
      "Epoch 55/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0100 - acc: 0.9964 - val_loss: 0.2667 - val_acc: 0.9422\n",
      "Epoch 56/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0094 - acc: 0.9964 - val_loss: 0.2444 - val_acc: 0.9545\n",
      "Epoch 57/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0102 - acc: 0.9965 - val_loss: 0.3690 - val_acc: 0.9310\n",
      "Epoch 58/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0090 - acc: 0.9968 - val_loss: 0.2127 - val_acc: 0.9587\n",
      "Epoch 59/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0121 - acc: 0.9958 - val_loss: 0.2702 - val_acc: 0.9408\n",
      "Epoch 60/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0087 - acc: 0.9969 - val_loss: 0.3024 - val_acc: 0.9397\n",
      "Epoch 61/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0092 - acc: 0.9968 - val_loss: 0.2339 - val_acc: 0.9543\n",
      "Epoch 62/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0103 - acc: 0.9962 - val_loss: 0.3410 - val_acc: 0.9327\n",
      "Epoch 63/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0084 - acc: 0.9971 - val_loss: 0.3458 - val_acc: 0.9382\n",
      "Epoch 64/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0092 - acc: 0.9969 - val_loss: 0.2697 - val_acc: 0.9532\n",
      "Epoch 65/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0091 - acc: 0.9969 - val_loss: 0.2918 - val_acc: 0.9412\n",
      "Epoch 66/75\n",
      "109150/109150 [==============================] - 6s 51us/step - loss: 0.0084 - acc: 0.9970 - val_loss: 0.3374 - val_acc: 0.9287\n",
      "Epoch 67/75\n",
      "109150/109150 [==============================] - 6s 50us/step - loss: 0.0100 - acc: 0.9965 - val_loss: 0.3604 - val_acc: 0.9200\n",
      "Epoch 68/75\n",
      "109150/109150 [==============================] - 5s 50us/step - loss: 0.0088 - acc: 0.9969 - val_loss: 0.3088 - val_acc: 0.9428\n",
      "Epoch 69/75\n",
      "109150/109150 [==============================] - 5s 50us/step - loss: 0.0102 - acc: 0.9963 - val_loss: 0.2381 - val_acc: 0.9547\n",
      "Epoch 70/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0111 - acc: 0.9958 - val_loss: 0.2225 - val_acc: 0.9558\n",
      "Epoch 71/75\n",
      "109150/109150 [==============================] - 6s 52us/step - loss: 0.0088 - acc: 0.9971 - val_loss: 0.2783 - val_acc: 0.9470\n",
      "Epoch 72/75\n",
      "109150/109150 [==============================] - 5s 50us/step - loss: 0.0061 - acc: 0.9978 - val_loss: 0.3181 - val_acc: 0.9453\n",
      "Epoch 73/75\n",
      "109150/109150 [==============================] - 5s 50us/step - loss: 0.0057 - acc: 0.9980 - val_loss: 0.2651 - val_acc: 0.9423\n",
      "Epoch 74/75\n",
      "109150/109150 [==============================] - 5s 50us/step - loss: 0.0070 - acc: 0.9976 - val_loss: 0.3661 - val_acc: 0.9422\n",
      "Epoch 75/75\n",
      "109150/109150 [==============================] - 5s 50us/step - loss: 0.0106 - acc: 0.9963 - val_loss: 0.4432 - val_acc: 0.9153\n"
     ]
    }
   ],
   "source": [
    "# from livelossplot import PlotLossesKeras # for visualizing the loss and accuracy\n",
    "\n",
    "history = model.fit(X_train, y_train, \n",
    "                    epochs=75, \n",
    "                    batch_size=batch_size, \n",
    "                    verbose=1, \n",
    "                    validation_data=(X_test, y_test), \n",
    "                    callbacks=[lrate]) # callbacks=[PlotLossesKeras()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制准确率\n",
    "plt.plot(history.history['acc'])  \n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')  \n",
    "plt.ylabel('accuracy')  \n",
    "plt.xlabel('epoch')  \n",
    "plt.xlim(0, 80)\n",
    "plt.legend(['train', 'validation'], loc='lower right') \n",
    "plt.show()  \n",
    "\n",
    "# 绘制 loss \n",
    "plt.plot(history.history['loss'])  \n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')  \n",
    "plt.ylabel('loss')  \n",
    "plt.xlabel('epoch')  \n",
    "plt.xlim(0, 80)\n",
    "plt.legend(['train', 'validation'], loc='upper right')  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test, batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.99      0.88       800\n",
      "           1       0.99      0.78      0.87       800\n",
      "           2       0.87      0.96      0.92       800\n",
      "           3       0.99      0.85      0.92       800\n",
      "           4       1.00      0.99      0.99       800\n",
      "\n",
      "   micro avg       0.92      0.92      0.92      4000\n",
      "   macro avg       0.93      0.92      0.92      4000\n",
      "weighted avg       0.93      0.92      0.92      4000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test.argmax(axis=1), y_pred.argmax(axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ranking-based average precision: 0.954\n",
      "Rnaking loss: 0.028\n",
      "Coverge_error: 1.112\n"
     ]
    }
   ],
   "source": [
    "print(\"ranking-based average precision: {:.3f}\".format(label_ranking_average_precision_score(y_test.todense(), y_pred)))\n",
    "print(\"Rnaking loss: {:.3f}\".format(label_ranking_loss(y_test.todense(), y_pred)))\n",
    "print(\"Coverge_error: {:.3f}\".format(coverage_error(y_test.todense(), y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting 'normalize=True'.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "        \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    \n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(10,10))\n",
    "plot_confusion_matrix(cnf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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